MachineLearning.json

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Encourage others who create new posts for questions to post here instead!\n\nThread will stay alive until next one so keep posting after the date in the title.\n\nThanks to everyone for answering questions in the previous thread!", "author_fullname": "t2_6l4z3", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] Simple Questions Thread", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_12wcr8i", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.92, "author_flair_background_color": null, "subreddit_type": "public", "ups": 53, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 53, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": true, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1682262020.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Please post your questions here instead of creating a new thread. 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"selftext": "paper:  [\\[2305.02301\\] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (arxiv.org)](https://arxiv.org/abs/2305.02301) \n\nAbstract: \n\n> Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for small models within a multi-task training framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our 770M T5 model outperforms the 540B PaLM model using only 80% of available data on a benchmark task.", "author_fullname": "t2_ulx7x1ju", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[R] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "three", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": true, "name": "t3_1381gd3", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.9, "author_flair_background_color": null, "subreddit_type": "public", "ups": 7, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Research", "can_mod_post": false, "score": 7, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683237630.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>paper:  <a href=\"https://arxiv.org/abs/2305.02301\">[2305.02301] Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes (arxiv.org)</a> </p>\n\n<p>Abstract: </p>\n\n<blockquote>\n<p>Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for small models within a multi-task training framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our 770M T5 model outperforms the 540B PaLM model using only 80% of available data on a benchmark task.</p>\n</blockquote>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?auto=webp&v=enabled&s=757c00601aa4ffb984c87000927a0610d04c3845", "width": 1200, "height": 700}, "resolutions": [{"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=108&crop=smart&auto=webp&v=enabled&s=586089b93aa59ebd86bb3b273ad1fb0c73e45ab7", "width": 108, "height": 63}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=216&crop=smart&auto=webp&v=enabled&s=00869aa5692fb9c8aa11f48ed92bff8db4f47293", "width": 216, "height": 126}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=320&crop=smart&auto=webp&v=enabled&s=72f6ae2c0800df8a56c3fc74afb033bf37cc16a9", "width": 320, "height": 186}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=640&crop=smart&auto=webp&v=enabled&s=cfcb5f9f66743f2e26952e5edff4dfed984af692", "width": 640, "height": 373}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=960&crop=smart&auto=webp&v=enabled&s=821ed287940b59a56b2643dcaf6a356ccfdc4eb5", "width": 960, "height": 560}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=1080&crop=smart&auto=webp&v=enabled&s=f101972ffc7ec2e3eedefa45eaa677e4d9024520", "width": 1080, "height": 630}], "variants": {}, "id": "q3evP6JeDpAC2MdSQHWYxnCYTqbJkElIQsLFqVSdkss"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "1381gd3", "is_robot_indexable": true, "report_reasons": null, "author": "Dapper_Cherry1025", "discussion_type": null, "num_comments": 2, "send_replies": false, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/1381gd3/r_distilling_stepbystep_outperforming_larger/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/1381gd3/r_distilling_stepbystep_outperforming_larger/", "subreddit_subscribers": 2649887, "created_utc": 1683237630.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "During  the recent earnings call, Mark Zuckerberg answered a question from Eric  Sheridan of Goldman Sachs on Meta's AI strategy, opportunities to  integrate into products, and why they open source models and how it  would benefit their business.\n\nI found the reasoning to be very sound and promising for the OSS and AI community.\n\nThe  biggest risk from AI, in my opinion, is not the doomsday scenarios that  intuitively come to mind but rather that the most powerful AI systems  will only be accessible to the most powerful and resourceful  corporations.\n\nQuote copied from Ben Thompson's write up on Meta's earning in his [Stratechery blog post](https://stratechery.com/2023/facebook-earnings-generative-ai-and-messaging-monetization-open-source-and-ai/) which goes beyond AI. *It's behind a paywall but I highly recommend it personally.*\n\nSome noteworthy quotes that signal the thought process at Meta FAIR and more broadly\n\n* We\u2019re just playing a different game on the infrastructure  than companies like Google or Microsoft or Amazon\n* We would aspire to and hope to make even more open than that. So, we\u2019ll need to figure out a way to do that.\n* ...lead us to do more work in terms of open sourcing, some of the lower level models and tools\n* Open sourcing low level tools make the way we run all this infrastructure more efficient over time.\n* On  PyTorch: It\u2019s generally been very valuable for us to provide that  because now  all of the best developers across the industry are using  tools that  we\u2019re also using internally.\n* I would expect us to be pushing and helping  to build out an open ecosystem.\n\nFor  all the negative that comes out of the popular discourse on Meta, I  think their work to open source key tech tools over the last 10 years  has been exceptional, here's hoping it continues into this decade of AI  and pushes other tech giants to also realize the benefits of Open  Source.\n\nFull Transcript:\n\n>Right  now most of the companies that are training large language  models have  business models that lead them to a closed approach to development. I  think **there\u2019s an** **important opportunity to help create an  open ecosystem.**  If we can help be a part of this, then much of the  industry will  standardize on using these open tools and help improve  them further. So  this will make it easier for other companies to  integrate with our  products and platforms as we enable more  integrations, and that will  help our products stay at the leading edge  as well.  \nOur  approach to AI and our infrastructure has always been fairly  open. We  open source many of our state of the art models so people can   experiment and build with them. This quarter we released our LLaMa LLM   to researchers. It has 65 billion parameters but outperforms larger   models and has proven quite popular. We\u2019ve also open-sourced three other   groundbreaking visual models along with their training data and model   weights \u2014 Segment Anything, DinoV2, and our Animated Drawings tool \u2014  and  we\u2019ve gotten positive feedback on all of those as well.  \nI  think that there\u2019s an important distinction between the products we  offer and a lot of the technical infrastructure, especially the software  that we write to support that. And historically, whether it\u2019s the Open  Compute project that we\u2019ve done or just open sourcing a lot of the   infrastructure that we\u2019ve built, we\u2019ve historically open sourced a lot   of that infrastructure, even though we haven\u2019t open sourced the code for   our core products or anything like that.  \nAnd the reason why I think why we do this is that unlike some of  the other companies in the space, **we\u2019re not selling a cloud computing service** **where we try to keep the different software infrastructure that we\u2019re building proprietary.** For us, **it\u2019s way better if the industry  standardizes on the basic tools that we\u2019re using**  and therefore we can benefit from the improvements that others make and  others\u2019 use of those tools can, in some cases like Open Compute, **drive down the costs** of  those things which make our business more efficient too. So I think to  some degree **we\u2019re just playing a different game** on the infrastructure  than companies like Google or Microsoft or Amazon, and that creates different incentives for us.  \nSo overall, I think **that that\u2019s going to lead us to do more work in terms of open sourcing, some of the lower level models and tools**.  But of  course, a lot of the product work itself is going to be  specific and  integrated with the things that we do. So it\u2019s not that  everything we do is going to be open. Obviously, a bunch of this needs  to be developed in a way that creates unique value for our products, but  I think in  terms of the basic models, **I would expect us to be pushing and helping  to build out an open ecosystem** here, which I think is something that\u2019s  going to be important.  \nOn the AI tools, and we have a bunch of history here, right? So if you  if you look at what we\u2019ve done with **PyTorch**,  for example, which has  generally become the standard in the industry  as a tool that a lot of  folks who are building AI models and different  things in that space use,  **it\u2019s generally been very valuable** for us to provide that because now  all of the **best developers across the industry are using tools that  we\u2019re also using internally**.  So the tool chain is the same. So when they create some innovation, we  can easily integrate it into the things that we\u2019re doing. When we  improve something, it improves other products too. Because it\u2019s  integrated with our technology stack, when there are opportunities to  make integrations with products, it\u2019s much easier to  make sure that  developers and other folks are compatible with the things  that we need  in the way that our systems work.  \nSo there are a lot of advantages, but **I view this more as a kind of back end infrastructure advantage with potential integrations on the  product side**,  but one that should hopefully enable us to stay at the  leading edge  and integrate more broadly with the community and also make  the way we  run all this infrastructure more efficient over time. There  are a  number of models. I just gave PyTorch as an example. Open Compute  is  another model that has worked really well for us in this way, both to   incorporate both innovation and scale efficiency into our own   infrastructure.  \nSo I think that  there\u2019s, our incentives I think are basically  aligned towards moving in  this direction. Now that said, there\u2019s a lot  to figure out, right? So  when you asked if there are going to be other opportunities, I hope so. I  can\u2019t speak to what all those things might  be now. This is all quite  early in getting developed. **The better we do at the foundational work, the more opportunities** I think that will come and present themselves. So I think that that\u2019s all stuff that we need to  figure out. But at least **at the base level, I think we\u2019re generally incentivized to move in this direction**. And we also need to figure out  how to go in that direction over time.  \nI  mean, I mentioned LLaMA before and I also want to be clear that  while  I\u2019m talking about helping contribute to an open ecosystem, LLaMA  is a  model that we only really made available to researchers and there\u2019s  a  lot of really good stuff that\u2019s happening there. But a lot of the  work  that we\u2019re doing, I think, **we would aspire to and hope to make even more open than that. So, we\u2019ll need to figure out a way to do that.**", "author_fullname": "t2_gcv8u7kg", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[Discussion]: Mark Zuckerberg on Meta's Strategy on Open Source and AI during the earnings call", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_1373nhq", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.95, "author_flair_background_color": null, "subreddit_type": "public", "ups": 382, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 382, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683157697.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": true, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>During  the recent earnings call, Mark Zuckerberg answered a question from Eric  Sheridan of Goldman Sachs on Meta's AI strategy, opportunities to  integrate into products, and why they open source models and how it  would benefit their business.</p>\n\n<p>I found the reasoning to be very sound and promising for the OSS and AI community.</p>\n\n<p>The  biggest risk from AI, in my opinion, is not the doomsday scenarios that  intuitively come to mind but rather that the most powerful AI systems  will only be accessible to the most powerful and resourceful  corporations.</p>\n\n<p>Quote copied from Ben Thompson's write up on Meta's earning in his <a href=\"https://stratechery.com/2023/facebook-earnings-generative-ai-and-messaging-monetization-open-source-and-ai/\">Stratechery blog post</a> which goes beyond AI. <em>It's behind a paywall but I highly recommend it personally.</em></p>\n\n<p>Some noteworthy quotes that signal the thought process at Meta FAIR and more broadly</p>\n\n<ul>\n<li>We\u2019re just playing a different game on the infrastructure  than companies like Google or Microsoft or Amazon</li>\n<li>We would aspire to and hope to make even more open than that. So, we\u2019ll need to figure out a way to do that.</li>\n<li>...lead us to do more work in terms of open sourcing, some of the lower level models and tools</li>\n<li>Open sourcing low level tools make the way we run all this infrastructure more efficient over time.</li>\n<li>On  PyTorch: It\u2019s generally been very valuable for us to provide that  because now  all of the best developers across the industry are using  tools that  we\u2019re also using internally.</li>\n<li>I would expect us to be pushing and helping  to build out an open ecosystem.</li>\n</ul>\n\n<p>For  all the negative that comes out of the popular discourse on Meta, I  think their work to open source key tech tools over the last 10 years  has been exceptional, here's hoping it continues into this decade of AI  and pushes other tech giants to also realize the benefits of Open  Source.</p>\n\n<p>Full Transcript:</p>\n\n<blockquote>\n<p>Right  now most of the companies that are training large language  models have  business models that lead them to a closed approach to development. I  think <strong>there\u2019s an</strong> <strong>important opportunity to help create an  open ecosystem.</strong>  If we can help be a part of this, then much of the  industry will  standardize on using these open tools and help improve  them further. So  this will make it easier for other companies to  integrate with our  products and platforms as we enable more  integrations, and that will  help our products stay at the leading edge  as well.<br/>\nOur  approach to AI and our infrastructure has always been fairly  open. We  open source many of our state of the art models so people can   experiment and build with them. This quarter we released our LLaMa LLM   to researchers. It has 65 billion parameters but outperforms larger   models and has proven quite popular. We\u2019ve also open-sourced three other   groundbreaking visual models along with their training data and model   weights \u2014 Segment Anything, DinoV2, and our Animated Drawings tool \u2014  and  we\u2019ve gotten positive feedback on all of those as well.<br/>\nI  think that there\u2019s an important distinction between the products we  offer and a lot of the technical infrastructure, especially the software  that we write to support that. And historically, whether it\u2019s the Open  Compute project that we\u2019ve done or just open sourcing a lot of the   infrastructure that we\u2019ve built, we\u2019ve historically open sourced a lot   of that infrastructure, even though we haven\u2019t open sourced the code for   our core products or anything like that.<br/>\nAnd the reason why I think why we do this is that unlike some of  the other companies in the space, <strong>we\u2019re not selling a cloud computing service</strong> <strong>where we try to keep the different software infrastructure that we\u2019re building proprietary.</strong> For us, <strong>it\u2019s way better if the industry  standardizes on the basic tools that we\u2019re using</strong>  and therefore we can benefit from the improvements that others make and  others\u2019 use of those tools can, in some cases like Open Compute, <strong>drive down the costs</strong> of  those things which make our business more efficient too. So I think to  some degree <strong>we\u2019re just playing a different game</strong> on the infrastructure  than companies like Google or Microsoft or Amazon, and that creates different incentives for us.<br/>\nSo overall, I think <strong>that that\u2019s going to lead us to do more work in terms of open sourcing, some of the lower level models and tools</strong>.  But of  course, a lot of the product work itself is going to be  specific and  integrated with the things that we do. So it\u2019s not that  everything we do is going to be open. Obviously, a bunch of this needs  to be developed in a way that creates unique value for our products, but  I think in  terms of the basic models, <strong>I would expect us to be pushing and helping  to build out an open ecosystem</strong> here, which I think is something that\u2019s  going to be important.<br/>\nOn the AI tools, and we have a bunch of history here, right? So if you  if you look at what we\u2019ve done with <strong>PyTorch</strong>,  for example, which has  generally become the standard in the industry  as a tool that a lot of  folks who are building AI models and different  things in that space use,  <strong>it\u2019s generally been very valuable</strong> for us to provide that because now  all of the <strong>best developers across the industry are using tools that  we\u2019re also using internally</strong>.  So the tool chain is the same. So when they create some innovation, we  can easily integrate it into the things that we\u2019re doing. When we  improve something, it improves other products too. Because it\u2019s  integrated with our technology stack, when there are opportunities to  make integrations with products, it\u2019s much easier to  make sure that  developers and other folks are compatible with the things  that we need  in the way that our systems work.<br/>\nSo there are a lot of advantages, but <strong>I view this more as a kind of back end infrastructure advantage with potential integrations on the  product side</strong>,  but one that should hopefully enable us to stay at the  leading edge  and integrate more broadly with the community and also make  the way we  run all this infrastructure more efficient over time. There  are a  number of models. I just gave PyTorch as an example. Open Compute  is  another model that has worked really well for us in this way, both to   incorporate both innovation and scale efficiency into our own   infrastructure.<br/>\nSo I think that  there\u2019s, our incentives I think are basically  aligned towards moving in  this direction. Now that said, there\u2019s a lot  to figure out, right? So  when you asked if there are going to be other opportunities, I hope so. I  can\u2019t speak to what all those things might  be now. This is all quite  early in getting developed. <strong>The better we do at the foundational work, the more opportunities</strong> I think that will come and present themselves. So I think that that\u2019s all stuff that we need to  figure out. But at least <strong>at the base level, I think we\u2019re generally incentivized to move in this direction</strong>. And we also need to figure out  how to go in that direction over time.<br/>\nI  mean, I mentioned LLaMA before and I also want to be clear that  while  I\u2019m talking about helping contribute to an open ecosystem, LLaMA  is a  model that we only really made available to researchers and there\u2019s  a  lot of really good stuff that\u2019s happening there. But a lot of the  work  that we\u2019re doing, I think, <strong>we would aspire to and hope to make even more open than that. So, we\u2019ll need to figure out a way to do that.</strong></p>\n</blockquote>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/QYVcsNs7Hz5CSrOdaWs_uGEQEFA4gzUCQJejC0g3bM0.jpg?auto=webp&v=enabled&s=0df3e61abeb29a290424706e3464ed61fb270bd5", "width": 1280, "height": 584}, "resolutions": [{"url": "https://external-preview.redd.it/QYVcsNs7Hz5CSrOdaWs_uGEQEFA4gzUCQJejC0g3bM0.jpg?width=108&crop=smart&auto=webp&v=enabled&s=bfd9cd591d20ca5922b844e0149e47b3ea274db1", "width": 108, "height": 49}, {"url": "https://external-preview.redd.it/QYVcsNs7Hz5CSrOdaWs_uGEQEFA4gzUCQJejC0g3bM0.jpg?width=216&crop=smart&auto=webp&v=enabled&s=238cfb113cb8f3776c8cc764047e30b291b6ca15", "width": 216, "height": 98}, {"url": 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false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "1373nhq", "is_robot_indexable": true, "report_reasons": null, "author": "noiseinvacuum", "discussion_type": null, "num_comments": 79, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/1373nhq/discussion_mark_zuckerberg_on_metas_strategy_on/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/1373nhq/discussion_mark_zuckerberg_on_metas_strategy_on/", "subreddit_subscribers": 2649887, "created_utc": 1683157697.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "", "author_fullname": "t2_8no51cpv", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[R] Fully Autonomous Programming with Large Language Models", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "three", "downs": 0, "thumbnail_height": 81, "top_awarded_type": null, "hide_score": false, "name": "t3_137odqz", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.9, "author_flair_background_color": null, "subreddit_type": "public", "ups": 24, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": 140, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Research", "can_mod_post": false, "score": 24, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "https://b.thumbs.redditmedia.com/xJIDdRPbdg8MfKT05Zy5jXKuiLfqqqVfgLvrJ1dYfKQ.jpg", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "link", "content_categories": null, "is_self": false, "mod_note": null, "created": 1683212038.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "arxiv.org", "allow_live_comments": false, "selftext_html": null, "likes": null, "suggested_sort": null, "banned_at_utc": null, "url_overridden_by_dest": "https://arxiv.org/abs/2304.10423", "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": 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"height": 373}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=960&crop=smart&auto=webp&v=enabled&s=821ed287940b59a56b2643dcaf6a356ccfdc4eb5", "width": 960, "height": 560}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=1080&crop=smart&auto=webp&v=enabled&s=f101972ffc7ec2e3eedefa45eaa677e4d9024520", "width": 1080, "height": 630}], "variants": {}, "id": "q3evP6JeDpAC2MdSQHWYxnCYTqbJkElIQsLFqVSdkss"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "137odqz", "is_robot_indexable": true, "report_reasons": null, "author": "vadimdotme", "discussion_type": null, "num_comments": 6, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/137odqz/r_fully_autonomous_programming_with_large/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://arxiv.org/abs/2304.10423", "subreddit_subscribers": 2649887, "created_utc": 1683212038.0, "num_crossposts": 1, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "This is a great and easily read paper. LLMs do the task described here really well. And I didn't realize how useful that could be.", "author_fullname": "t2_1vrx", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "Prediction and Entropy of Printed English. Shannon 1951", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": null, "downs": 0, "thumbnail_height": 140, "top_awarded_type": null, "hide_score": false, "name": "t3_137zz3j", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 1.0, "author_flair_background_color": "", "subreddit_type": "public", "ups": 2, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": 140, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": null, "can_mod_post": false, "score": 2, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "https://b.thumbs.redditmedia.com/vtW-Wj5InNA1TVBgl81ItO23Mni3koqhuI3d4Qgr26s.jpg", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "link", "content_categories": null, "is_self": false, "mod_note": null, "created": 1683234359.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "archive.org", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>This is a great and easily read paper. LLMs do the task described here really well. And I didn't realize how useful that could be.</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "url_overridden_by_dest": "https://archive.org/details/bstj30-1-50", "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/dnuh_p3E2m03rjn7olupSyORrbzy5H7hYnuydOnm4bw.jpg?auto=webp&v=enabled&s=1a8218fc117cf33205bd94b20d16bfdfe0d498ad", "width": 180, "height": 306}, "resolutions": [{"url": "https://external-preview.redd.it/dnuh_p3E2m03rjn7olupSyORrbzy5H7hYnuydOnm4bw.jpg?width=108&crop=smart&auto=webp&v=enabled&s=d400751c2aa630a1507707a5a95622c3a84cfea3", "width": 108, "height": 183}], "variants": {}, "id": "_OHskOevb_b6AifDpIjS2HG56SVZWXBdLLHJS3zsW8g"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": "Mod to 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paper, we propose a novel method to combine Large Language Models with Information Retrieval  to improve the accuracy, credibility and traceability of LLM-generated content!\n\nPaper: [https://arxiv.org/abs/2304.14732](https://arxiv.org/abs/2304.14732)\n\n​\n\nhttps://preview.redd.it/t5kdmrna3txa1.png?width=1431&format=png&auto=webp&v=enabled&s=fa52e9bd9f9d5ae892509f551f1ef63234bb77ff", "author_fullname": "t2_ftkfvcyo", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[Research] Towards Accurate, Credible and Traceable Large Language Models\uff01\uff01\uff01", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "three", "downs": 0, "thumbnail_height": 60, "top_awarded_type": null, "hide_score": false, "media_metadata": {"t5kdmrna3txa1": {"status": "valid", "e": "Image", "m": "image/png", "p": [{"y": 46, "x": 108, "u": 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"https://preview.redd.it/t5kdmrna3txa1.png?width=1431&format=png&auto=webp&v=enabled&s=fa52e9bd9f9d5ae892509f551f1ef63234bb77ff"}, "id": "t5kdmrna3txa1"}}, "name": "t3_137iyxk", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.84, "author_flair_background_color": null, "subreddit_type": "public", "ups": 8, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": 140, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Research", "can_mod_post": false, "score": 8, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "https://b.thumbs.redditmedia.com/ppCNkUwiMvqwykseG8Z2933zVxO78yyr-PbYb5cZKbg.jpg", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683202882.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Hello everyone, in this paper, we propose a novel method to combine Large Language Models with Information Retrieval  to improve the accuracy, credibility and traceability of LLM-generated content!</p>\n\n<p>Paper: <a href=\"https://arxiv.org/abs/2304.14732\">https://arxiv.org/abs/2304.14732</a></p>\n\n<p>​</p>\n\n<p><a href=\"https://preview.redd.it/t5kdmrna3txa1.png?width=1431&format=png&auto=webp&v=enabled&s=fa52e9bd9f9d5ae892509f551f1ef63234bb77ff\">https://preview.redd.it/t5kdmrna3txa1.png?width=1431&format=png&auto=webp&v=enabled&s=fa52e9bd9f9d5ae892509f551f1ef63234bb77ff</a></p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "137iyxk", "is_robot_indexable": true, "report_reasons": null, "author": "Latter-Confidence595", "discussion_type": null, "num_comments": 3, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/137iyxk/research_towards_accurate_credible_and_traceable/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/137iyxk/research_towards_accurate_credible_and_traceable/", "subreddit_subscribers": 2649887, "created_utc": 1683202882.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "Greetings\u00a0r/MachineLearning!\n\nThis is Doruk from Oblivus, and I'm excited to announce the launch of our platform, Oblivus Cloud. After more than a year of beta testing, we're excited to offer you a platform where you can deploy affordable and scalable GPU virtual machines in as little as 30 seconds! We believe that Oblivus Cloud is the perfect alternative to other cloud service providers when it comes to training your ML models.\n\n[https://oblivus.com/cloud](https://oblivus.com/cloud)\n\n\ud83e\udd14\u00a0**What sets Oblivus Cloud apart?**\n\nAt the start of our journey, we had two primary goals in mind: to democratize High-Performance Computing and make it as straightforward as possible. We understand that maintaining GPU servers through major cloud service providers can be expensive, with hidden fees adding to the burden of running and maintaining servers.\n\nAdditionally, the cloud can sometimes be overly complex for individuals who don't have much knowledge but still require powerful computing resources.\n\nThat's why we decided to create a platform that offers affordable pricing, easy usability, and high-quality performance. Oblivus Cloud provides just that - a simple, affordable, and high-quality alternative for anyone in need of powerful computing resources.\n\n\u26aa\u00a0**Features**\n\nOblivus Cloud comes packed with a wide range of features to make your experience smooth, seamless, and fully customizable. Here are some of the key features you can expect:\n\n1. Fully customizable infrastructure that lets you switch between CPU and GPU configurations to suit your needs. You can easily modify server components and scale your virtual machine up and down in seconds.\n2. No quotas or complex verification processes. Whether you represent a company, an institution, or you're a researcher, you have full access to our infrastructure without any limitations.\n3. Each virtual machine comes with 10Gbps to 40Gbps public network connectivity.\n4. Transparent and affordable per-minute-based Pay-As-You-Go pricing with no hidden fees. Plus, free data ingress and egress. (Pricing:\u00a0[https://oblivus.com/pricing/](https://oblivus.com/pricing/))\n5. Optimized cost with storage and IP address-only billing when the virtual machine is shut down.\n6. NVMe ($0.00011/GB/hr) and HDD ($0.00006/GB/hr) local and network storage that is 3x replicated to fulfill your storage needs.\n7. Choose from a variety of cutting-edge CPUs and 10 state-of-the-art GPU SKUs. (Availability:\u00a0[https://oblivus.com/availability/](https://oblivus.com/availability/))\n8. Access our infrastructure from three data center locations in Chicago, New York City, and Las Vegas. (Data Centers:\u00a0[https://oblivus.com/datacenters/](https://oblivus.com/datacenters/))\n9. OblivusAI OS images come with pre-installed ML libraries, so you can start training your models right away without the hassle of installing and configuring the necessary libraries.\n10. If you're working with a team, utilize our organization feature to simplify the billing process. Everyone in your organization uses the same billing profile, so you don't need to keep track of multiple accounts.\n11. Easy-to-use API with detailed documentation so that you can integrate your code with ours.\n12. In addition to on-demand servers, we also offer Reserved Instances if your computing needs don't change often, giving you access to more discounts.\n\n\ud83d\udcb2\u00a0**Pricing**\n\nAt Oblivus Cloud, we provide pricing that is affordable, transparent, and up to 80% cheaper than major cloud service providers, while still offering the computing power you need for your machine learning models. Here is a breakdown of our pricing:\n\n1. CPU-based virtual machines starting from just $0.019/hour.\n2. NVIDIA Quadro RTX 4000s starting from $0.27/hour.\n3. Tesla V100s starting from $0.51/hour.\n4. NVIDIA A40s and RTX A6000s starting from $1.41/hour.\n5. NVIDIA A100s starting from $2.25/hour.\n\nWe also offer 5 other GPU SKUs to help you accurately size your workloads and only pay for what you need. Say goodbye to hidden fees and unpredictable costs.\n\nIf you represent a company, be sure to register for a business account to access even better pricing rates. ([https://console.oblivus.com/business/](https://console.oblivus.com/business/))\n\n\ud83c\udf8a\u00a0**Promo Code**\n\nJoin us in celebrating the launch of Oblivus Cloud by claiming your $1 free credit! This may sound small, but it's enough to get started with us and experience the power of our platform. With $1, you can get over 3 hours of computing on our most affordable GPU-based configuration, or over 50 hours of computing on our cheapest CPU-based configuration.\n\nTo redeem this free credit, simply use the code REDDIT\\_1 on the 'Add Balance' page after registration.\n\nRegister now at\u00a0[https://console.oblivus.com/register](https://console.oblivus.com/register)\n\n\ud83d\udd17\u00a0**Quick Links**\n\nWebsite:\u00a0[https://oblivus.com/](https://oblivus.com/)\n\nConsole:\u00a0[https://console.oblivus.com/](https://console.oblivus.com/)\n\nCompany Documentation:\u00a0[https://docs.oblivus.com/](https://docs.oblivus.com/)\n\nAPI Documentation:\u00a0[https://documenter.getpostman.com/view/21699896/UzBtoQ3e](https://documenter.getpostman.com/view/21699896/UzBtoQ3e)\n\nIf you have any questions, feel free to post them below and I'll be happy to assist you.", "author_fullname": "t2_4h5j3lz1", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] Oblivus Cloud | Scalable GPU servers from $0.29/hr", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": null, "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_1370xg9", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.88, "author_flair_background_color": null, "subreddit_type": "public", "ups": 120, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": null, "can_mod_post": false, "score": 120, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683151148.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Greetings\u00a0r/MachineLearning!</p>\n\n<p>This is Doruk from Oblivus, and I'm excited to announce the launch of our platform, Oblivus Cloud. After more than a year of beta testing, we're excited to offer you a platform where you can deploy affordable and scalable GPU virtual machines in as little as 30 seconds! We believe that Oblivus Cloud is the perfect alternative to other cloud service providers when it comes to training your ML models.</p>\n\n<p><a href=\"https://oblivus.com/cloud\">https://oblivus.com/cloud</a></p>\n\n<p>\ud83e\udd14\u00a0<strong>What sets Oblivus Cloud apart?</strong></p>\n\n<p>At the start of our journey, we had two primary goals in mind: to democratize High-Performance Computing and make it as straightforward as possible. We understand that maintaining GPU servers through major cloud service providers can be expensive, with hidden fees adding to the burden of running and maintaining servers.</p>\n\n<p>Additionally, the cloud can sometimes be overly complex for individuals who don't have much knowledge but still require powerful computing resources.</p>\n\n<p>That's why we decided to create a platform that offers affordable pricing, easy usability, and high-quality performance. Oblivus Cloud provides just that - a simple, affordable, and high-quality alternative for anyone in need of powerful computing resources.</p>\n\n<p>\u26aa\u00a0<strong>Features</strong></p>\n\n<p>Oblivus Cloud comes packed with a wide range of features to make your experience smooth, seamless, and fully customizable. Here are some of the key features you can expect:</p>\n\n<ol>\n<li>Fully customizable infrastructure that lets you switch between CPU and GPU configurations to suit your needs. You can easily modify server components and scale your virtual machine up and down in seconds.</li>\n<li>No quotas or complex verification processes. Whether you represent a company, an institution, or you're a researcher, you have full access to our infrastructure without any limitations.</li>\n<li>Each virtual machine comes with 10Gbps to 40Gbps public network connectivity.</li>\n<li>Transparent and affordable per-minute-based Pay-As-You-Go pricing with no hidden fees. Plus, free data ingress and egress. (Pricing:\u00a0<a href=\"https://oblivus.com/pricing/\">https://oblivus.com/pricing/</a>)</li>\n<li>Optimized cost with storage and IP address-only billing when the virtual machine is shut down.</li>\n<li>NVMe ($0.00011/GB/hr) and HDD ($0.00006/GB/hr) local and network storage that is 3x replicated to fulfill your storage needs.</li>\n<li>Choose from a variety of cutting-edge CPUs and 10 state-of-the-art GPU SKUs. (Availability:\u00a0<a href=\"https://oblivus.com/availability/\">https://oblivus.com/availability/</a>)</li>\n<li>Access our infrastructure from three data center locations in Chicago, New York City, and Las Vegas. (Data Centers:\u00a0<a href=\"https://oblivus.com/datacenters/\">https://oblivus.com/datacenters/</a>)</li>\n<li>OblivusAI OS images come with pre-installed ML libraries, so you can start training your models right away without the hassle of installing and configuring the necessary libraries.</li>\n<li>If you're working with a team, utilize our organization feature to simplify the billing process. Everyone in your organization uses the same billing profile, so you don't need to keep track of multiple accounts.</li>\n<li>Easy-to-use API with detailed documentation so that you can integrate your code with ours.</li>\n<li>In addition to on-demand servers, we also offer Reserved Instances if your computing needs don't change often, giving you access to more discounts.</li>\n</ol>\n\n<p>\ud83d\udcb2\u00a0<strong>Pricing</strong></p>\n\n<p>At Oblivus Cloud, we provide pricing that is affordable, transparent, and up to 80% cheaper than major cloud service providers, while still offering the computing power you need for your machine learning models. Here is a breakdown of our pricing:</p>\n\n<ol>\n<li>CPU-based virtual machines starting from just $0.019/hour.</li>\n<li>NVIDIA Quadro RTX 4000s starting from $0.27/hour.</li>\n<li>Tesla V100s starting from $0.51/hour.</li>\n<li>NVIDIA A40s and RTX A6000s starting from $1.41/hour.</li>\n<li>NVIDIA A100s starting from $2.25/hour.</li>\n</ol>\n\n<p>We also offer 5 other GPU SKUs to help you accurately size your workloads and only pay for what you need. Say goodbye to hidden fees and unpredictable costs.</p>\n\n<p>If you represent a company, be sure to register for a business account to access even better pricing rates. (<a href=\"https://console.oblivus.com/business/\">https://console.oblivus.com/business/</a>)</p>\n\n<p>\ud83c\udf8a\u00a0<strong>Promo Code</strong></p>\n\n<p>Join us in celebrating the launch of Oblivus Cloud by claiming your $1 free credit! This may sound small, but it's enough to get started with us and experience the power of our platform. With $1, you can get over 3 hours of computing on our most affordable GPU-based configuration, or over 50 hours of computing on our cheapest CPU-based configuration.</p>\n\n<p>To redeem this free credit, simply use the code REDDIT_1 on the 'Add Balance' page after registration.</p>\n\n<p>Register now at\u00a0<a href=\"https://console.oblivus.com/register\">https://console.oblivus.com/register</a></p>\n\n<p>\ud83d\udd17\u00a0<strong>Quick Links</strong></p>\n\n<p>Website:\u00a0<a href=\"https://oblivus.com/\">https://oblivus.com/</a></p>\n\n<p>Console:\u00a0<a href=\"https://console.oblivus.com/\">https://console.oblivus.com/</a></p>\n\n<p>Company Documentation:\u00a0<a href=\"https://docs.oblivus.com/\">https://docs.oblivus.com/</a></p>\n\n<p>API Documentation:\u00a0<a href=\"https://documenter.getpostman.com/view/21699896/UzBtoQ3e\">https://documenter.getpostman.com/view/21699896/UzBtoQ3e</a></p>\n\n<p>If you have any questions, feel free to post them below and I'll be happy to assist you.</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/J1ZbK7tBY27zxv-h-zUR-lVZ9v5GYOGFaFJ59I8v3Dc.jpg?auto=webp&v=enabled&s=71db9d1602c23647e6fd41c5f2e4e4ce55772427", "width": 500, "height": 500}, "resolutions": [{"url": "https://external-preview.redd.it/J1ZbK7tBY27zxv-h-zUR-lVZ9v5GYOGFaFJ59I8v3Dc.jpg?width=108&crop=smart&auto=webp&v=enabled&s=65c2e9a9cf2d441a28b80f16e78111d133ab21cf", "width": 108, "height": 108}, {"url": "https://external-preview.redd.it/J1ZbK7tBY27zxv-h-zUR-lVZ9v5GYOGFaFJ59I8v3Dc.jpg?width=216&crop=smart&auto=webp&v=enabled&s=c345922c965405d0b58d95a6eae1ee82393febc6", "width": 216, "height": 216}, {"url": "https://external-preview.redd.it/J1ZbK7tBY27zxv-h-zUR-lVZ9v5GYOGFaFJ59I8v3Dc.jpg?width=320&crop=smart&auto=webp&v=enabled&s=60aeaec85fdd5f7894798c33cc8128dc26a33e30", "width": 320, "height": 320}], "variants": {}, "id": "KIA3gR2c0onCn4dJnSlCAslcUlYiK_J8n7o-cI7Ck0Y"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "1370xg9", "is_robot_indexable": true, "report_reasons": null, "author": "dorukalpulgen", "discussion_type": null, "num_comments": 17, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/1370xg9/d_oblivus_cloud_scalable_gpu_servers_from_029hr/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/1370xg9/d_oblivus_cloud_scalable_gpu_servers_from_029hr/", "subreddit_subscribers": 2649887, "created_utc": 1683151148.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "Paper: [https://arxiv.org/abs/2304.13731](https://arxiv.org/abs/2304.13731)\n\nCode: [https://github.com/declare-lab/tango](https://github.com/declare-lab/tango)\n\nDemo: [https://huggingface.co/spaces/declare-lab/tango](https://huggingface.co/spaces/declare-lab/tango)\n\nProject: [https://tango-web.github.io/](https://tango-web.github.io/)\n\nAbstract: The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction- and chain-of-thought-based fine-tuning, that has significantly improved zero- and few-shot performance in many natural language processing (NLP) tasks. Inspired by such successes, we adopt such an instruction-tuned LLM FLAN-T5 as the text encoder for text-to audio (TTA) generation\u2014a task where the goal is to generate an audio from its textual description. The prior works on TTA either pre-trained a joint text-audio encoder or used a non-instruction-tuned model, such as, T5. Consequently, our latent diffusion model (LDM)-based approach (TANGO) outperforms the state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set, despite training the LDM on a 63 times smaller dataset and keeping the text encoder frozen. This improvement might also be attributed to the adoption of audio pressure level based sound mixing for training set augmentation, whereas the prior methods take a random mix.  \n\n\nhttps://preview.redd.it/uzioyoqpfuxa1.png?width=7784&format=png&auto=webp&v=enabled&s=e667cfa557f77552c6657a799edb651ee7febf6c", "author_fullname": "t2_1ou19exa", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[Research] [Project] Text-to-Audio Generation using Instruction-Tuned LLM and Latent Diffusion Model", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "three", "downs": 0, "thumbnail_height": 76, "top_awarded_type": null, "hide_score": false, "media_metadata": {"uzioyoqpfuxa1": {"status": "valid", "e": "Image", "m": "image/png", "p": [{"y": 59, "x": 108, "u": "https://preview.redd.it/uzioyoqpfuxa1.png?width=108&crop=smart&auto=webp&v=enabled&s=3a3d112ae100f1a77aa1003613b9d1ca01238271"}, {"y": 118, "x": 216, "u": "https://preview.redd.it/uzioyoqpfuxa1.png?width=216&crop=smart&auto=webp&v=enabled&s=052873d9118c9b1109d578176c05d6cdce3cbda3"}, {"y": 175, "x": 320, "u": "https://preview.redd.it/uzioyoqpfuxa1.png?width=320&crop=smart&auto=webp&v=enabled&s=c7347f97074a49b58c36ac6912b91abd6a667c7f"}, {"y": 350, "x": 640, "u": "https://preview.redd.it/uzioyoqpfuxa1.png?width=640&crop=smart&auto=webp&v=enabled&s=1dc9c1ca6b6f0c80d174feb662ac94f571450b7f"}, {"y": 525, "x": 960, "u": "https://preview.redd.it/uzioyoqpfuxa1.png?width=960&crop=smart&auto=webp&v=enabled&s=97a3a24e70c8c8a6f912d04890352b193eef3de6"}, {"y": 590, "x": 1080, "u": "https://preview.redd.it/uzioyoqpfuxa1.png?width=1080&crop=smart&auto=webp&v=enabled&s=99617d7fa597d8b6f3da5d4ba1a1391e03a2ac37"}], "s": {"y": 4258, "x": 7784, "u": "https://preview.redd.it/uzioyoqpfuxa1.png?width=7784&format=png&auto=webp&v=enabled&s=e667cfa557f77552c6657a799edb651ee7febf6c"}, "id": "uzioyoqpfuxa1"}}, "name": "t3_137ssn6", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 1.0, "author_flair_background_color": null, "subreddit_type": "public", "ups": 2, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": 140, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Research", "can_mod_post": false, "score": 2, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "https://a.thumbs.redditmedia.com/mIZhARpV59m8AhO6ZniniKhYVor3eXYi-11kLL4qJq0.jpg", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683218687.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Paper: <a href=\"https://arxiv.org/abs/2304.13731\">https://arxiv.org/abs/2304.13731</a></p>\n\n<p>Code: <a href=\"https://github.com/declare-lab/tango\">https://github.com/declare-lab/tango</a></p>\n\n<p>Demo: <a href=\"https://huggingface.co/spaces/declare-lab/tango\">https://huggingface.co/spaces/declare-lab/tango</a></p>\n\n<p>Project: <a href=\"https://tango-web.github.io/\">https://tango-web.github.io/</a></p>\n\n<p>Abstract: The immense scale of the recent large language models (LLM) allows many interesting properties, such as, instruction- and chain-of-thought-based fine-tuning, that has significantly improved zero- and few-shot performance in many natural language processing (NLP) tasks. Inspired by such successes, we adopt such an instruction-tuned LLM FLAN-T5 as the text encoder for text-to audio (TTA) generation\u2014a task where the goal is to generate an audio from its textual description. The prior works on TTA either pre-trained a joint text-audio encoder or used a non-instruction-tuned model, such as, T5. Consequently, our latent diffusion model (LDM)-based approach (TANGO) outperforms the state-of-the-art AudioLDM on most metrics and stays comparable on the rest on AudioCaps test set, despite training the LDM on a 63 times smaller dataset and keeping the text encoder frozen. This improvement might also be attributed to the adoption of audio pressure level based sound mixing for training set augmentation, whereas the prior methods take a random mix.  </p>\n\n<p><a href=\"https://preview.redd.it/uzioyoqpfuxa1.png?width=7784&format=png&auto=webp&v=enabled&s=e667cfa557f77552c6657a799edb651ee7febf6c\">https://preview.redd.it/uzioyoqpfuxa1.png?width=7784&format=png&auto=webp&v=enabled&s=e667cfa557f77552c6657a799edb651ee7febf6c</a></p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "137ssn6", "is_robot_indexable": true, "report_reasons": null, "author": "bideex", "discussion_type": null, "num_comments": 1, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/137ssn6/research_project_texttoaudio_generation_using/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/137ssn6/research_project_texttoaudio_generation_using/", "subreddit_subscribers": 2649887, "created_utc": 1683218687.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "May 9 at 12 pm ET (16:00 UTC), join **Matt Welsh**, CEO and Co-founder of Fixie.ai, for the free ACM TechTalk \"[**Large Language Models and the End of Programming**](https://acm-org.zoom.us/webinar/register/6516831450157/WN_vf0SPZY7TeWMH-5_IaloIQ).\"\n\nMatt believes that most software will eventually be replaced by AI models that, given an appropriate description of a task, will directly execute that task, without requiring the creation or maintenance of conventional software. In effect, large language models act as a virtual machine that is \u201cprogrammed\u201d in natural language. This talk will explore the implications of this prediction, drawing on recent research into the cognitive and task execution capabilities of large language models.\n\n[Register](https://acm-org.zoom.us/webinar/register/6516831450157/WN_vf0SPZY7TeWMH-5_IaloIQ) to attend this talk live or on demand.", "author_fullname": "t2_cd4qjhv", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[N] May 9, Free Talk with Matt Welsh, \"Large Language Models and the End of Programming\"", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "two", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_137jfvl", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.64, "author_flair_background_color": null, "subreddit_type": "public", "ups": 4, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "News", "can_mod_post": false, "score": 4, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683204052.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>May 9 at 12 pm ET (16:00 UTC), join <strong>Matt Welsh</strong>, CEO and Co-founder of Fixie.ai, for the free ACM TechTalk "<a href=\"https://acm-org.zoom.us/webinar/register/6516831450157/WN_vf0SPZY7TeWMH-5_IaloIQ\"><strong>Large Language Models and the End of Programming</strong></a>."</p>\n\n<p>Matt believes that most software will eventually be replaced by AI models that, given an appropriate description of a task, will directly execute that task, without requiring the creation or maintenance of conventional software. In effect, large language models act as a virtual machine that is \u201cprogrammed\u201d in natural language. This talk will explore the implications of this prediction, drawing on recent research into the cognitive and task execution capabilities of large language models.</p>\n\n<p><a href=\"https://acm-org.zoom.us/webinar/register/6516831450157/WN_vf0SPZY7TeWMH-5_IaloIQ\">Register</a> to attend this talk live or on demand.</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/-TdXzS9iAsjf4dUR8tBzLdampPsR6fNnRPpBR3xMsmE.jpg?auto=webp&v=enabled&s=244c59d4d7c0afb6b0df199d693b7efda7162db6", "width": 60, "height": 60}, "resolutions": [], "variants": {}, "id": "Ow6yc0WkWVXCRxHUhhRq4t7_TN18fLOlAO-3jycS2Z4"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "137jfvl", "is_robot_indexable": true, "report_reasons": null, "author": "ACMLearning", "discussion_type": null, "num_comments": 3, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/137jfvl/n_may_9_free_talk_with_matt_welsh_large_language/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/137jfvl/n_may_9_free_talk_with_matt_welsh_large_language/", "subreddit_subscribers": 2649887, "created_utc": 1683204052.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "I am interested in hearing your thoughts on this.", "author_fullname": "t2_jfpfcdq", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] With powerful general models like SAM starting to roll out, is computer vision close to being solved?", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_137uu2u", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.4, "author_flair_background_color": null, "subreddit_type": "public", "ups": 0, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 0, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683223126.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>I am interested in hearing your thoughts on this.</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "137uu2u", "is_robot_indexable": true, "report_reasons": null, "author": "AmroMustafa", "discussion_type": null, "num_comments": 2, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/137uu2u/d_with_powerful_general_models_like_sam_starting/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/137uu2u/d_with_powerful_general_models_like_sam_starting/", "subreddit_subscribers": 2649887, "created_utc": 1683223126.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "https://github.com/openlm-research/open_llama\n\n> We train our models on the RedPajama dataset released by Together, which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA.", "author_fullname": "t2_4c2jd", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[N] OpenLLaMA: An Open Reproduction of LLaMA", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "two", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136exj2", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.98, "author_flair_background_color": null, "subreddit_type": "public", "ups": 369, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "News", "can_mod_post": false, "score": 369, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683103871.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": true, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p><a href=\"https://github.com/openlm-research/open_llama\">https://github.com/openlm-research/open_llama</a></p>\n\n<blockquote>\n<p>We train our models on the RedPajama dataset released by Together, which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA.</p>\n</blockquote>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/Ng00yjsbRmRyRWeHK_4CTXBXszTLsRglLwKqIQtla6s.jpg?auto=webp&v=enabled&s=2bc4fd71de132e349f6ffd5256d55454ad33ab75", "width": 1200, "height": 600}, "resolutions": [{"url": "https://external-preview.redd.it/Ng00yjsbRmRyRWeHK_4CTXBXszTLsRglLwKqIQtla6s.jpg?width=108&crop=smart&auto=webp&v=enabled&s=b44904d76a777c8d259d61b8d1551a9e112f1eff", "width": 108, "height": 54}, {"url": "https://external-preview.redd.it/Ng00yjsbRmRyRWeHK_4CTXBXszTLsRglLwKqIQtla6s.jpg?width=216&crop=smart&auto=webp&v=enabled&s=b0fa21efb615d371700fbe08be7c588b2f0fc467", "width": 216, "height": 108}, {"url": "https://external-preview.redd.it/Ng00yjsbRmRyRWeHK_4CTXBXszTLsRglLwKqIQtla6s.jpg?width=320&crop=smart&auto=webp&v=enabled&s=a47958d353586624a9d76a18710f2f6eb0aded74", "width": 320, "height": 160}, {"url": "https://external-preview.redd.it/Ng00yjsbRmRyRWeHK_4CTXBXszTLsRglLwKqIQtla6s.jpg?width=640&crop=smart&auto=webp&v=enabled&s=26af5b9f91661246008f80462157da3df9c87b64", "width": 640, "height": 320}, {"url": "https://external-preview.redd.it/Ng00yjsbRmRyRWeHK_4CTXBXszTLsRglLwKqIQtla6s.jpg?width=960&crop=smart&auto=webp&v=enabled&s=9ce58966f5188803d401c06ede2f6534c01943f4", "width": 960, "height": 480}, {"url": "https://external-preview.redd.it/Ng00yjsbRmRyRWeHK_4CTXBXszTLsRglLwKqIQtla6s.jpg?width=1080&crop=smart&auto=webp&v=enabled&s=c5486d074b004e8024a817708e5bcc0c7e4637f4", "width": 1080, "height": 540}], "variants": {}, "id": "KJnUUPQpnEnDKu0A5Sw9G-jaINpdDpYBE5WTlDKBJwU"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "136exj2", "is_robot_indexable": true, "report_reasons": null, "author": "Philpax", "discussion_type": null, "num_comments": 96, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136exj2/n_openllama_an_open_reproduction_of_llama/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136exj2/n_openllama_an_open_reproduction_of_llama/", "subreddit_subscribers": 2649887, "created_utc": 1683103871.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "Hey everyone!\n\nChatGPT and other large language models (LLMs) have been making headlines left and right, which has made it somewhat challenging to find clear, concise information on the topic. To this end, my colleague decided to put together a **review** that covers the full story of LLMs and Reinforcement Learning from Human Feedback (RLHF):\n\n[**The Full Story of Large Language Models and RLHF**](https://www.assemblyai.com/blog/the-full-story-of-large-language-models-and-rlhf/)\n\nHe discusses everything from the foundations to the latest advancements in an attempt to make it accessible for anyone interested in the topic. We'd love to hear your thoughts on the topic!", "author_fullname": "t2_h366h3z5", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] The Full Story of Large Language Models and RLHF", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136qdh9", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.89, "author_flair_background_color": null, "subreddit_type": "public", "ups": 48, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 48, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683127321.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Hey everyone!</p>\n\n<p>ChatGPT and other large language models (LLMs) have been making headlines left and right, which has made it somewhat challenging to find clear, concise information on the topic. To this end, my colleague decided to put together a <strong>review</strong> that covers the full story of LLMs and Reinforcement Learning from Human Feedback (RLHF):</p>\n\n<p><a href=\"https://www.assemblyai.com/blog/the-full-story-of-large-language-models-and-rlhf/\"><strong>The Full Story of Large Language Models and RLHF</strong></a></p>\n\n<p>He discusses everything from the foundations to the latest advancements in an attempt to make it accessible for anyone interested in the topic. We'd love to hear your thoughts on the topic!</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/RZ7Ms1Xj2xtjrwsxu2dQckz5CkzZ1O1mW1Hczl_jT34.jpg?auto=webp&v=enabled&s=88680270c005f51cbcd0547e9d5c58103dc8adad", "width": 1600, "height": 900}, "resolutions": [{"url": "https://external-preview.redd.it/RZ7Ms1Xj2xtjrwsxu2dQckz5CkzZ1O1mW1Hczl_jT34.jpg?width=108&crop=smart&auto=webp&v=enabled&s=0f42d7ffbfd7ddd4781b17115cd2a26c6669e7d1", "width": 108, "height": 60}, {"url": "https://external-preview.redd.it/RZ7Ms1Xj2xtjrwsxu2dQckz5CkzZ1O1mW1Hczl_jT34.jpg?width=216&crop=smart&auto=webp&v=enabled&s=27b2a2d31938b690b2216db303d6f60dbb3b7f93", "width": 216, "height": 121}, {"url": 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false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "136qdh9", "is_robot_indexable": true, "report_reasons": null, "author": "SleekEagle", "discussion_type": null, "num_comments": 16, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136qdh9/d_the_full_story_of_large_language_models_and_rlhf/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136qdh9/d_the_full_story_of_large_language_models_and_rlhf/", "subreddit_subscribers": 2649887, "created_utc": 1683127321.0, "num_crossposts": 1, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "apologies for the ramble, wanted to think through this problem a little bit.\n\ndriverless cars, while they are currently pretty good and arguably have a lower accident rate than people, consensus seems to be that they will 'occasionally try to kill you' and currently require constant supervision. they fail to adapt to edge cases that most humans can reason about pretty accurately. \n\nfor example, we can easily identify angry drivers, and give them plenty of room. we can also adapt to changes in pedestrian behavior (there appears to be a parade going on today, so i should reroute or expect increased pedestrian traffic)\n\ntheres already a small theory-of-mind component at play, even if it is hard coded (at a 4-way stop, is that guy going to go first or is he waiting for me?)\n\nnot a huge stretch of the imagination to ruminate that cars will need some kind of general human behavior model like an LLM to increase safety in edge cases to human-level or beyond\n\nthis is a bit of an aside, but with fast enough compute, driverless cars could even perform explainable moral reasoning in advance of all the silly train-problem scenarios driverless cars bring up (in this contrived scenario, do i hit a grandma or a baby?), a written log of why it chooses a specific action in the moments before it does it could be helpful in iteration and alignment.\n\nthoughts?", "author_fullname": "t2_sdnih", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] will driverless cars need good theory of mind to function safer than humans?", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_137sj3y", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 0.17, "author_flair_background_color": null, "subreddit_type": "public", "ups": 0, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 0, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683218100.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>apologies for the ramble, wanted to think through this problem a little bit.</p>\n\n<p>driverless cars, while they are currently pretty good and arguably have a lower accident rate than people, consensus seems to be that they will 'occasionally try to kill you' and currently require constant supervision. they fail to adapt to edge cases that most humans can reason about pretty accurately. </p>\n\n<p>for example, we can easily identify angry drivers, and give them plenty of room. we can also adapt to changes in pedestrian behavior (there appears to be a parade going on today, so i should reroute or expect increased pedestrian traffic)</p>\n\n<p>theres already a small theory-of-mind component at play, even if it is hard coded (at a 4-way stop, is that guy going to go first or is he waiting for me?)</p>\n\n<p>not a huge stretch of the imagination to ruminate that cars will need some kind of general human behavior model like an LLM to increase safety in edge cases to human-level or beyond</p>\n\n<p>this is a bit of an aside, but with fast enough compute, driverless cars could even perform explainable moral reasoning in advance of all the silly train-problem scenarios driverless cars bring up (in this contrived scenario, do i hit a grandma or a baby?), a written log of why it chooses a specific action in the moments before it does it could be helpful in iteration and alignment.</p>\n\n<p>thoughts?</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "137sj3y", "is_robot_indexable": true, "report_reasons": null, "author": "Dankmemexplorer", "discussion_type": null, "num_comments": 5, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/137sj3y/d_will_driverless_cars_need_good_theory_of_mind/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/137sj3y/d_will_driverless_cars_need_good_theory_of_mind/", "subreddit_subscribers": 2649887, "created_utc": 1683218100.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "", "author_fullname": "t2_hwj8c", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[R] Poisoning Language Models During Instruction Tuning", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", 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"created": 1683117510.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "arxiv.org", "allow_live_comments": false, "selftext_html": null, "likes": null, "suggested_sort": null, "banned_at_utc": null, "url_overridden_by_dest": "https://arxiv.org/abs/2305.00944", "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?auto=webp&v=enabled&s=757c00601aa4ffb984c87000927a0610d04c3845", "width": 1200, "height": 700}, "resolutions": [{"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=108&crop=smart&auto=webp&v=enabled&s=586089b93aa59ebd86bb3b273ad1fb0c73e45ab7", "width": 108, "height": 63}, {"url": 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"parent_whitelist_status": "all_ads", "stickied": false, "url": "https://arxiv.org/abs/2305.00944", "subreddit_subscribers": 2649887, "created_utc": 1683117510.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "**TL;DR the alpaca dataset has some issues, and the code was super slow. I updated it to be much faster, and it supports the chat completion API so you can use gpt-3.5-turbo for 1/10 the cost as well as gpt-4, and it uses the databricks dolly 15k dataset for samples.**\n\n### Project/data resources\n\n* [GitHub Repo](https://github.com/jondurbin/airoboros)\n* [100k synthetic prompts, gpt-3.5-turbo](https://storage.googleapis.com/airoboros-dump/gpt-3.5-turbo-100k/instructions.jsonl)\n* [random seed topics used](https://storage.googleapis.com/airoboros-dump/gpt-3.5-turbo-100k/topics.txt)\n\n### Usage\n\n(Python) install: `pip install airoboros`\n\nBe sure to set `OPENAI_API_KEY` or pass it as CLI arg.\n\nGenerate prompts with: `airoboros generate-instructions`\n\n### Initial run info\n\nThe first 100k prompts were generated in under 24 hours, using gpt-3.5-turbo and about $200 in OpenAI API usage.  I haven't had time yet to really deep dive into the results to do any QA, so it could be complete trash.\n\nThe dataset is obviously subject to OpenAI's ToS, so keep that in mind if you fine-tune any models with it.\n\nAnyone want to help?\n* quality checks on the data, prompt/code updates to remediate issues...  I realize this dataset will surely have some issues, but what's more interesting to me is how it compares to alpaca and/or alpaca-gpt4\n* generating instructions with gpt-4 instead of gpt-3.5-turbo - I'm still on the waitlist unfortunately, be VERY careful as this will rip through your usage limits quickly\n* fine tune llama or other models for (for research purposes of course)", "author_fullname": "t2_uqhzj3m9", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[P] airoboros: a rewrite of self-instruct/alpaca synthetic prompt generation", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "four", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136vt7b", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 0.87, "author_flair_background_color": null, "subreddit_type": "public", "ups": 11, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Project", "can_mod_post": false, "score": 11, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683139543.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p><strong>TL;DR the alpaca dataset has some issues, and the code was super slow. I updated it to be much faster, and it supports the chat completion API so you can use gpt-3.5-turbo for 1/10 the cost as well as gpt-4, and it uses the databricks dolly 15k dataset for samples.</strong></p>\n\n<h3>Project/data resources</h3>\n\n<ul>\n<li><a href=\"https://github.com/jondurbin/airoboros\">GitHub Repo</a></li>\n<li><a href=\"https://storage.googleapis.com/airoboros-dump/gpt-3.5-turbo-100k/instructions.jsonl\">100k synthetic prompts, gpt-3.5-turbo</a></li>\n<li><a href=\"https://storage.googleapis.com/airoboros-dump/gpt-3.5-turbo-100k/topics.txt\">random seed topics used</a></li>\n</ul>\n\n<h3>Usage</h3>\n\n<p>(Python) install: <code>pip install airoboros</code></p>\n\n<p>Be sure to set <code>OPENAI_API_KEY</code> or pass it as CLI arg.</p>\n\n<p>Generate prompts with: <code>airoboros generate-instructions</code></p>\n\n<h3>Initial run info</h3>\n\n<p>The first 100k prompts were generated in under 24 hours, using gpt-3.5-turbo and about $200 in OpenAI API usage.  I haven't had time yet to really deep dive into the results to do any QA, so it could be complete trash.</p>\n\n<p>The dataset is obviously subject to OpenAI's ToS, so keep that in mind if you fine-tune any models with it.</p>\n\n<p>Anyone want to help?\n* quality checks on the data, prompt/code updates to remediate issues...  I realize this dataset will surely have some issues, but what's more interesting to me is how it compares to alpaca and/or alpaca-gpt4\n* generating instructions with gpt-4 instead of gpt-3.5-turbo - I'm still on the waitlist unfortunately, be VERY careful as this will rip through your usage limits quickly\n* fine tune llama or other models for (for research purposes of course)</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/8PxqEaWKbaFvKgnsxAVjYnmLXWdm2armVnDnei0jPtY.jpg?auto=webp&v=enabled&s=5005bb864a18e4a9abb8eb36d8e88b63fe91e37d", "width": 1200, "height": 600}, "resolutions": [{"url": "https://external-preview.redd.it/8PxqEaWKbaFvKgnsxAVjYnmLXWdm2armVnDnei0jPtY.jpg?width=108&crop=smart&auto=webp&v=enabled&s=e1bbcc3ccbb665aa9dae57db363f4daeec5760e5", "width": 108, "height": 54}, {"url": "https://external-preview.redd.it/8PxqEaWKbaFvKgnsxAVjYnmLXWdm2armVnDnei0jPtY.jpg?width=216&crop=smart&auto=webp&v=enabled&s=ffb329d9f6d5b3c6c07493c445050f31e16344ed", "width": 216, "height": 108}, {"url": "https://external-preview.redd.it/8PxqEaWKbaFvKgnsxAVjYnmLXWdm2armVnDnei0jPtY.jpg?width=320&crop=smart&auto=webp&v=enabled&s=79efaed56664c26805f594eb63677bef4cba0f61", "width": 320, "height": 160}, {"url": "https://external-preview.redd.it/8PxqEaWKbaFvKgnsxAVjYnmLXWdm2armVnDnei0jPtY.jpg?width=640&crop=smart&auto=webp&v=enabled&s=e0a06794c62907f36b037dcf5c2ef084e62e1777", "width": 640, "height": 320}, {"url": "https://external-preview.redd.it/8PxqEaWKbaFvKgnsxAVjYnmLXWdm2armVnDnei0jPtY.jpg?width=960&crop=smart&auto=webp&v=enabled&s=e3e2a244882a7ed87af8837325fc36f806d8f24c", "width": 960, "height": 480}, {"url": "https://external-preview.redd.it/8PxqEaWKbaFvKgnsxAVjYnmLXWdm2armVnDnei0jPtY.jpg?width=1080&crop=smart&auto=webp&v=enabled&s=4364e1fcd9cef07c7d2e4eac87cb4ad54653c2ff", "width": 1080, "height": 540}], "variants": {}, "id": "G9fL_SFBHYksZ3EKH5BKUoZdLxIRUJVJMA-cupKWA24"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "136vt7b", "is_robot_indexable": true, "report_reasons": null, "author": "JonDurbin", "discussion_type": null, "num_comments": 0, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136vt7b/p_airoboros_a_rewrite_of_selfinstructalpaca/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136vt7b/p_airoboros_a_rewrite_of_selfinstructalpaca/", "subreddit_subscribers": 2649887, "created_utc": 1683139543.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "A little bit of context: we have a few hundred thousand IoT devices that push timeseries data that gets consumed by our users. We'd like to implement some anomaly detection models, and maybe some predictive models in the future.\n\nMy question specific comes because just this morning I noticed in AWS CloudWatch that an anomaly detection alarm noted that it had finished training on limited metric data for my specific metric. Does this mean that for our data, we need some way to train a separate model for each IoT device's timeseries data? It makes sense that that is the case. The follow up question is how do people usually handle storing and retrieving these models efficiently for each IoT device?\n\ntl;dr what are strategies that the industry uses for training and storing many different trained models?", "author_fullname": "t2_4ifi7cx9", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] Training time-series data from IoT fleets on the fly", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136soc1", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 1.0, "author_flair_background_color": null, "subreddit_type": "public", "ups": 13, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 13, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683132442.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>A little bit of context: we have a few hundred thousand IoT devices that push timeseries data that gets consumed by our users. We'd like to implement some anomaly detection models, and maybe some predictive models in the future.</p>\n\n<p>My question specific comes because just this morning I noticed in AWS CloudWatch that an anomaly detection alarm noted that it had finished training on limited metric data for my specific metric. Does this mean that for our data, we need some way to train a separate model for each IoT device's timeseries data? It makes sense that that is the case. The follow up question is how do people usually handle storing and retrieving these models efficiently for each IoT device?</p>\n\n<p>tl;dr what are strategies that the industry uses for training and storing many different trained models?</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "136soc1", "is_robot_indexable": true, "report_reasons": null, "author": "sharddblade", "discussion_type": null, "num_comments": 4, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136soc1/d_training_timeseries_data_from_iot_fleets_on_the/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136soc1/d_training_timeseries_data_from_iot_fleets_on_the/", "subreddit_subscribers": 2649887, "created_utc": 1683132442.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "I am interest in using LangChain but I am also interested in creating my own thing. I love sticking Redis into things that I want to go fast. If it ain't first it's last. Why am I talking about Redis? Well, when I think about state, I would immediately want to go to a cache-based store. So, I don't get the \"state\" comments about LangChain. How are achieving state without a store? Also, this would be of a concern on a multiple instance container structure for scalability as well.\n\nWith that said, perhaps LangChain could be mixed in with a state store that is separated from the abstraction? If anyone's interested in a project adapter of that nature let me know.\n\nBack to LangChain, other than state what is it providing that is different than just building an api or service that interacts with an LLM such as ChatGPT.\n\nFrom the coding examples I just see a wrapper type functionality but what is it more under-the-hood on a high level that would be of note or interest? I trying to figure if there is utility to it or if perhaps another or more features to it would be desirable.", "author_fullname": "t2_58rkqicv", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[Discussion] Can someone on a high-level explain what someone can do in LangChain that they can't do in normal coding patterns? Is there opportunity for extension especially on state store.", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136rsog", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 0.79, "author_flair_background_color": null, "subreddit_type": "public", "ups": 13, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 13, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683130473.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>I am interest in using LangChain but I am also interested in creating my own thing. I love sticking Redis into things that I want to go fast. If it ain't first it's last. Why am I talking about Redis? Well, when I think about state, I would immediately want to go to a cache-based store. So, I don't get the "state" comments about LangChain. How are achieving state without a store? Also, this would be of a concern on a multiple instance container structure for scalability as well.</p>\n\n<p>With that said, perhaps LangChain could be mixed in with a state store that is separated from the abstraction? If anyone's interested in a project adapter of that nature let me know.</p>\n\n<p>Back to LangChain, other than state what is it providing that is different than just building an api or service that interacts with an LLM such as ChatGPT.</p>\n\n<p>From the coding examples I just see a wrapper type functionality but what is it more under-the-hood on a high level that would be of note or interest? I trying to figure if there is utility to it or if perhaps another or more features to it would be desirable.</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "136rsog", "is_robot_indexable": true, "report_reasons": null, "author": "Xtianus21", "discussion_type": null, "num_comments": 14, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136rsog/discussion_can_someone_on_a_highlevel_explain/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136rsog/discussion_can_someone_on_a_highlevel_explain/", "subreddit_subscribers": 2649887, "created_utc": 1683130473.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": " As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform  computations. \n\nConventional digital computers are struggling to keep up.\n\nAn analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy.\n\nSource: [https://gemm.ai/breaking-the-scaling-limits-of-analog-computing/](https://gemm.ai/breaking-the-scaling-limits-of-analog-computing/)", "author_fullname": "t2_vf2mow17", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[News] Breaking the scaling limits of analog computing", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "two", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136de7j", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 0.87, "author_flair_background_color": null, "subreddit_type": "public", "ups": 27, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "News", "can_mod_post": false, "score": 27, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683098362.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>As machine-learning models become larger and more complex, they require faster and more energy-efficient hardware to perform  computations. </p>\n\n<p>Conventional digital computers are struggling to keep up.</p>\n\n<p>An analog optical neural network could perform the same tasks as a digital one, such as image classification or speech recognition, but because computations are performed using light instead of electrical signals, optical neural networks can run many times faster while consuming less energy.</p>\n\n<p>Source: <a href=\"https://gemm.ai/breaking-the-scaling-limits-of-analog-computing/\">https://gemm.ai/breaking-the-scaling-limits-of-analog-computing/</a></p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/HnzF6oVS9qOXcdw_0u-mIQIUfxIyP_tDEHqdU7hDGvI.jpg?auto=webp&v=enabled&s=23e0747ea4500abb90f2d7bbdd73c5b551bb2517", "width": 2000, "height": 1333}, "resolutions": [{"url": "https://external-preview.redd.it/HnzF6oVS9qOXcdw_0u-mIQIUfxIyP_tDEHqdU7hDGvI.jpg?width=108&crop=smart&auto=webp&v=enabled&s=b78dda9fa02ab2a3b014f560d6f6b9571e726342", "width": 108, "height": 71}, {"url": "https://external-preview.redd.it/HnzF6oVS9qOXcdw_0u-mIQIUfxIyP_tDEHqdU7hDGvI.jpg?width=216&crop=smart&auto=webp&v=enabled&s=15a0e2f0cd52081c9a3497c796fb248220276ebb", "width": 216, "height": 143}, {"url": "https://external-preview.redd.it/HnzF6oVS9qOXcdw_0u-mIQIUfxIyP_tDEHqdU7hDGvI.jpg?width=320&crop=smart&auto=webp&v=enabled&s=a1efd06dbe65de0b21644df42034e1b1d618e245", "width": 320, "height": 213}, {"url": "https://external-preview.redd.it/HnzF6oVS9qOXcdw_0u-mIQIUfxIyP_tDEHqdU7hDGvI.jpg?width=640&crop=smart&auto=webp&v=enabled&s=991761dd3990ecbf3cceddee07523ac932b795a7", "width": 640, "height": 426}, {"url": "https://external-preview.redd.it/HnzF6oVS9qOXcdw_0u-mIQIUfxIyP_tDEHqdU7hDGvI.jpg?width=960&crop=smart&auto=webp&v=enabled&s=9de5683869d6f25083840e5ea672372afa1ecf85", "width": 960, "height": 639}, {"url": "https://external-preview.redd.it/HnzF6oVS9qOXcdw_0u-mIQIUfxIyP_tDEHqdU7hDGvI.jpg?width=1080&crop=smart&auto=webp&v=enabled&s=893d55e839628e60bdbf6b11657845ebbb40a5dc", "width": 1080, "height": 719}], "variants": {}, "id": "xae8V6ogQAgednRNu4knWm9ESIa5va6rGjcHsvnBfy4"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "136de7j", "is_robot_indexable": true, "report_reasons": null, "author": "gamefidelio", "discussion_type": null, "num_comments": 15, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136de7j/news_breaking_the_scaling_limits_of_analog/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136de7j/news_breaking_the_scaling_limits_of_analog/", "subreddit_subscribers": 2649887, "created_utc": 1683098362.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "Paper: [https://arxiv.org/abs/2305.00833](https://arxiv.org/abs/2305.00833) \n\nAbstract:\n\n>Large language models have been shown to struggle with limited context memory and multi-step reasoning. We propose a simple method for solving both of these problems by allowing the model to take Self-Notes. Unlike recent scratchpad approaches, the **model can deviate from the input context at any time to explicitly think.** This allows the model to recall information and perform reasoning on the fly as it reads the context, thus extending its memory and enabling multi-step reasoning. Our experiments on multiple tasks demonstrate that our method can successfully generalize to longer and more complicated instances from their training setup by taking Self-Notes at inference time. \n\nhttps://preview.redd.it/ace4s7rvvgxa1.jpg?width=1452&format=pjpg&auto=webp&v=enabled&s=b11532e8961a77cdbc936ae663537b3b2f22e8d4\n\nhttps://preview.redd.it/qw7xwcrvvgxa1.jpg?width=1317&format=pjpg&auto=webp&v=enabled&s=7a725fbefbf0e9d6a20cb0099f03138f1c8411cb\n\nhttps://preview.redd.it/btlwolqvvgxa1.jpg?width=1644&format=pjpg&auto=webp&v=enabled&s=5d087cdb9fbe76f9801d6f1dd6ff601428ec4234", "author_fullname": "t2_9l187vq4", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[R] Learning to Reason and Memorize with Self-Notes - Jack lanchantin et al Meta AI 2023", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "three", "downs": 0, "thumbnail_height": 51, "top_awarded_type": null, 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"link_flair_text_color": null, "upvote_ratio": 0.97, "author_flair_background_color": null, "subreddit_type": "public", "ups": 151, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": 140, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Research", "can_mod_post": false, "score": 151, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "https://b.thumbs.redditmedia.com/7kYAnx6Canp6oa4ep4eRRgTFQ_II_K0v4DXsdqQvE5g.jpg", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683054785.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Paper: <a href=\"https://arxiv.org/abs/2305.00833\">https://arxiv.org/abs/2305.00833</a> </p>\n\n<p>Abstract:</p>\n\n<blockquote>\n<p>Large language models have been shown to struggle with limited context memory and multi-step reasoning. We propose a simple method for solving both of these problems by allowing the model to take Self-Notes. Unlike recent scratchpad approaches, the <strong>model can deviate from the input context at any time to explicitly think.</strong> This allows the model to recall information and perform reasoning on the fly as it reads the context, thus extending its memory and enabling multi-step reasoning. Our experiments on multiple tasks demonstrate that our method can successfully generalize to longer and more complicated instances from their training setup by taking Self-Notes at inference time. </p>\n</blockquote>\n\n<p><a href=\"https://preview.redd.it/ace4s7rvvgxa1.jpg?width=1452&format=pjpg&auto=webp&v=enabled&s=b11532e8961a77cdbc936ae663537b3b2f22e8d4\">https://preview.redd.it/ace4s7rvvgxa1.jpg?width=1452&format=pjpg&auto=webp&v=enabled&s=b11532e8961a77cdbc936ae663537b3b2f22e8d4</a></p>\n\n<p><a href=\"https://preview.redd.it/qw7xwcrvvgxa1.jpg?width=1317&format=pjpg&auto=webp&v=enabled&s=7a725fbefbf0e9d6a20cb0099f03138f1c8411cb\">https://preview.redd.it/qw7xwcrvvgxa1.jpg?width=1317&format=pjpg&auto=webp&v=enabled&s=7a725fbefbf0e9d6a20cb0099f03138f1c8411cb</a></p>\n\n<p><a href=\"https://preview.redd.it/btlwolqvvgxa1.jpg?width=1644&format=pjpg&auto=webp&v=enabled&s=5d087cdb9fbe76f9801d6f1dd6ff601428ec4234\">https://preview.redd.it/btlwolqvvgxa1.jpg?width=1644&format=pjpg&auto=webp&v=enabled&s=5d087cdb9fbe76f9801d6f1dd6ff601428ec4234</a></p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "135xbbo", "is_robot_indexable": true, "report_reasons": null, "author": "Singularian2501", "discussion_type": null, "num_comments": 12, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/135xbbo/r_learning_to_reason_and_memorize_with_selfnotes/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/135xbbo/r_learning_to_reason_and_memorize_with_selfnotes/", "subreddit_subscribers": 2649887, "created_utc": 1683054785.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "> Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.\n\n[Arxiv version](https://arxiv.org/abs/2304.13385) [Official Version](https://www.sciencedirect.com/science/article/pii/S1361841523000683?dgcid=author)\n\nI am a co-author, PM for any questions.", "author_fullname": "t2_1j1kx1ex", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[R] ML Application to Low-Quality Brain Scans for Low-Income Countries", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "three", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136ic80", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 0.86, "author_flair_background_color": null, "subreddit_type": "public", "ups": 5, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Research", "can_mod_post": false, "score": 5, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "post_hint": "self", "content_categories": null, "is_self": true, "mod_note": null, "created": 1683114787.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><blockquote>\n<p>Low-field (<1T) magnetic resonance imaging (MRI) scanners remain in widespread use in low- and middle-income countries (LMICs) and are commonly used for some applications in higher income countries e.g. for small child patients with obesity, claustrophobia, implants, or tattoos. However, low-field MR images commonly have lower resolution and poorer contrast than images from high field (1.5T, 3T, and above). Here, we present Image Quality Transfer (IQT) to enhance low-field structural MRI by estimating from a low-field image the image we would have obtained from the same subject at high field. Our approach uses (i) a stochastic low-field image simulator as the forward model to capture uncertainty and variation in the contrast of low-field images corresponding to a particular high-field image, and (ii) an anisotropic U-Net variant specifically designed for the IQT inverse problem. We evaluate the proposed algorithm both in simulation and using multi-contrast (T1-weighted, T2-weighted, and fluid attenuated inversion recovery (FLAIR)) clinical low-field MRI data from an LMIC hospital. We show the efficacy of IQT in improving contrast and resolution of low-field MR images. We demonstrate that IQT-enhanced images have potential for enhancing visualisation of anatomical structures and pathological lesions of clinical relevance from the perspective of radiologists. IQT is proved to have capability of boosting the diagnostic value of low-field MRI, especially in low-resource settings.</p>\n</blockquote>\n\n<p><a href=\"https://arxiv.org/abs/2304.13385\">Arxiv version</a> <a href=\"https://www.sciencedirect.com/science/article/pii/S1361841523000683?dgcid=author\">Official Version</a></p>\n\n<p>I am a co-author, PM for any questions.</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "preview": {"images": [{"source": {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?auto=webp&v=enabled&s=757c00601aa4ffb984c87000927a0610d04c3845", "width": 1200, "height": 700}, "resolutions": [{"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=108&crop=smart&auto=webp&v=enabled&s=586089b93aa59ebd86bb3b273ad1fb0c73e45ab7", "width": 108, "height": 63}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=216&crop=smart&auto=webp&v=enabled&s=00869aa5692fb9c8aa11f48ed92bff8db4f47293", "width": 216, "height": 126}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=320&crop=smart&auto=webp&v=enabled&s=72f6ae2c0800df8a56c3fc74afb033bf37cc16a9", "width": 320, "height": 186}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=640&crop=smart&auto=webp&v=enabled&s=cfcb5f9f66743f2e26952e5edff4dfed984af692", "width": 640, "height": 373}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=960&crop=smart&auto=webp&v=enabled&s=821ed287940b59a56b2643dcaf6a356ccfdc4eb5", "width": 960, "height": 560}, {"url": "https://external-preview.redd.it/0HhwdU6MKIAKjL9Y8-B_iH374a3NiPTy0ib8lmloRzA.jpg?width=1080&crop=smart&auto=webp&v=enabled&s=f101972ffc7ec2e3eedefa45eaa677e4d9024520", "width": 1080, "height": 630}], "variants": {}, "id": "q3evP6JeDpAC2MdSQHWYxnCYTqbJkElIQsLFqVSdkss"}], "enabled": false}, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "136ic80", "is_robot_indexable": true, "report_reasons": null, "author": "sbb_ml", "discussion_type": null, "num_comments": 1, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136ic80/r_ml_application_to_lowquality_brain_scans_for/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136ic80/r_ml_application_to_lowquality_brain_scans_for/", "subreddit_subscribers": 2649887, "created_utc": 1683114787.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "First time poster, but facing an annoying problem. I have a dataset with startups and their descriptions and the aim is to classify these descriptions into their industry (fintech, proptech, biotech, gaming, etc). My industry dataset at first contained only 130 industry names, I then generated a list of 10 keywords associated with each industry and compared embeddings between the preprocessed descriptions and industry keywords to predict the industry the startup belongs to. \n\nThe biggest issue I face is the inability to find a suitable labelled dataset with company descriptions & associated labels. When I predict labels, I can only visually confirm or reject predictions which makes this quite wonky as you might imagine. There are some datasets on kaggle and on the web but they mostly focus on established industries such as mining, gold and accounting. Startup industries tend to be subdivisions of newer technologies and focus on a single issue, where larger companies might be involved in finance but also accounting. \n\nIn lieu of a dataset I can use, Id need to refine the industry keywords. I generated them with GPT4, and they are a little poor in terms of capturing the specific context of that industry. \n\nDoes anyone know of a dataset that I can use? Ive looked for two days and cant really find anything suitable. If no, does anyone have any idea of how to approach this problem in a different way or generating keywords better?", "author_fullname": "t2_ain7pthrh", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] Unable to find a proper dataset for classifying companies into their industry", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136kaiu", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 1.0, "author_flair_background_color": null, "subreddit_type": "public", "ups": 5, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 5, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683119834.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>First time poster, but facing an annoying problem. I have a dataset with startups and their descriptions and the aim is to classify these descriptions into their industry (fintech, proptech, biotech, gaming, etc). My industry dataset at first contained only 130 industry names, I then generated a list of 10 keywords associated with each industry and compared embeddings between the preprocessed descriptions and industry keywords to predict the industry the startup belongs to. </p>\n\n<p>The biggest issue I face is the inability to find a suitable labelled dataset with company descriptions & associated labels. When I predict labels, I can only visually confirm or reject predictions which makes this quite wonky as you might imagine. There are some datasets on kaggle and on the web but they mostly focus on established industries such as mining, gold and accounting. Startup industries tend to be subdivisions of newer technologies and focus on a single issue, where larger companies might be involved in finance but also accounting. </p>\n\n<p>In lieu of a dataset I can use, Id need to refine the industry keywords. I generated them with GPT4, and they are a little poor in terms of capturing the specific context of that industry. </p>\n\n<p>Does anyone know of a dataset that I can use? Ive looked for two days and cant really find anything suitable. If no, does anyone have any idea of how to approach this problem in a different way or generating keywords better?</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "136kaiu", "is_robot_indexable": true, "report_reasons": null, "author": "edgelord6942O", "discussion_type": null, "num_comments": 4, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136kaiu/d_unable_to_find_a_proper_dataset_for_classifying/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136kaiu/d_unable_to_find_a_proper_dataset_for_classifying/", "subreddit_subscribers": 2649887, "created_utc": 1683119834.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "How do papers accepted in Findings work for ACL? I know EMNLP allows authors with papers accepted to findings to submit to the co-located workshops and get a chance to present there. But the acceptance email of ACL said nothing about this. Is there anyone with experience from past ACL conferences?", "author_fullname": "t2_uwuignkd", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] Findings of ACL 2023: can we present in collocated workshops?", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136jp6q", "quarantine": false, "link_flair_text_color": null, "upvote_ratio": 0.86, "author_flair_background_color": null, "subreddit_type": "public", "ups": 5, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 5, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683118363.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>How do papers accepted in Findings work for ACL? I know EMNLP allows authors with papers accepted to findings to submit to the co-located workshops and get a chance to present there. But the acceptance email of ACL said nothing about this. Is there anyone with experience from past ACL conferences?</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": null, "id": "136jp6q", "is_robot_indexable": true, "report_reasons": null, "author": "ElektricDreamz", "discussion_type": null, "num_comments": 0, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136jp6q/d_findings_of_acl_2023_can_we_present_in/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136jp6q/d_findings_of_acl_2023_can_we_present_in/", "subreddit_subscribers": 2649887, "created_utc": 1683118363.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "Hello Redditors!\n\nIt's pretty well known that LLMs have solidified their place at the forefront of natural language processing, and are constantly pushing the boundaries of what is possible in terms of language understanding and generation.\n\nI spent some time playing around with the OpenAI fine-tuning API and I discovered that noisy data still has drastic effects even on powerful LLMs like Davinci.\n\n![img](9jrp0dvobgxa1 \"Improving fine-tuning accuracy by improving data quality.\n\")\n\nI wrote up a [quick article](https://www.kdnuggets.com/2023/04/finetuning-openai-language-models-noisily-labeled-data.html) in KDNuggets that shows how I used data-centric AI to automatically clean the noisy data in order to fine-tune a more robust OpenAI LLM. The resulting model has 37% fewer errors than the same LLM fine-tuned on the noisy data.\n\nLet me know what you think!", "author_fullname": "t2_s0qucgfk", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[N] Fine-Tuning OpenAI Language Models with Noisily Labeled Data (37% error reduction)", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "two", "downs": 0, "thumbnail_height": 74, "top_awarded_type": null, "hide_score": false, "media_metadata": {"9jrp0dvobgxa1": {"status": "valid", "e": "Image", "m": "image/png", "p": [{"y": 57, "x": 108, "u": "https://preview.redd.it/9jrp0dvobgxa1.png?width=108&crop=smart&auto=webp&v=enabled&s=0317b1e3516e8e17708ba1f86edd58bc71eb3aec"}, {"y": 115, "x": 216, "u": "https://preview.redd.it/9jrp0dvobgxa1.png?width=216&crop=smart&auto=webp&v=enabled&s=d43f5f5f029c862d5f1b811ed18b0f49c650eac8"}, {"y": 170, "x": 320, "u": "https://preview.redd.it/9jrp0dvobgxa1.png?width=320&crop=smart&auto=webp&v=enabled&s=6e2cb600587354402f55fa71bb849f0ba114e3df"}, {"y": 341, "x": 640, "u": "https://preview.redd.it/9jrp0dvobgxa1.png?width=640&crop=smart&auto=webp&v=enabled&s=1d23ada21932afef4be5263727c66495934da7c4"}, {"y": 512, "x": 960, "u": "https://preview.redd.it/9jrp0dvobgxa1.png?width=960&crop=smart&auto=webp&v=enabled&s=8a6e916af7c74896b7e454f13168c939463b3528"}, {"y": 576, "x": 1080, "u": "https://preview.redd.it/9jrp0dvobgxa1.png?width=1080&crop=smart&auto=webp&v=enabled&s=d1d9838ceda69feda550ddf63a2d866e6e4df70f"}], "s": {"y": 579, "x": 1085, "u": "https://preview.redd.it/9jrp0dvobgxa1.png?width=1085&format=png&auto=webp&v=enabled&s=f33123fb70db4d1eddd9a4b80f0abb995a300794"}, "id": "9jrp0dvobgxa1"}}, "name": "t3_135u6z5", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.92, "author_flair_background_color": null, "subreddit_type": "public", "ups": 144, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": 140, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "News", "can_mod_post": false, "score": 144, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "https://b.thumbs.redditmedia.com/OjcbBxHlGYMaUI9GXLf3s42yQ-1Y9RLoCnSISkWposU.jpg", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683047878.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>Hello Redditors!</p>\n\n<p>It's pretty well known that LLMs have solidified their place at the forefront of natural language processing, and are constantly pushing the boundaries of what is possible in terms of language understanding and generation.</p>\n\n<p>I spent some time playing around with the OpenAI fine-tuning API and I discovered that noisy data still has drastic effects even on powerful LLMs like Davinci.</p>\n\n<p>![img](9jrp0dvobgxa1 "Improving fine-tuning accuracy by improving data quality.\n")</p>\n\n<p>I wrote up a <a href=\"https://www.kdnuggets.com/2023/04/finetuning-openai-language-models-noisily-labeled-data.html\">quick article</a> in KDNuggets that shows how I used data-centric AI to automatically clean the noisy data in order to fine-tune a more robust OpenAI LLM. The resulting model has 37% fewer errors than the same LLM fine-tuned on the noisy data.</p>\n\n<p>Let me know what you think!</p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "135u6z5", "is_robot_indexable": true, "report_reasons": null, "author": "cmauck10", "discussion_type": null, "num_comments": 10, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/135u6z5/n_finetuning_openai_language_models_with_noisily/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/135u6z5/n_finetuning_openai_language_models_with_noisily/", "subreddit_subscribers": 2649887, "created_utc": 1683047878.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "", "author_fullname": "t2_3sq10s54", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "LLM learn personas, and personas can increase toxicity [R]", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "three", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_1377hmo", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.22, "author_flair_background_color": null, "subreddit_type": "public", "ups": 0, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Research", "can_mod_post": false, "score": 0, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "default", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": false, "mod_note": null, "created": 1683167862.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "arxiv.org", "allow_live_comments": false, "selftext_html": null, "likes": null, "suggested_sort": null, "banned_at_utc": null, "url_overridden_by_dest": "https://arxiv.org/pdf/2304.05335.pdf", "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "1377hmo", "is_robot_indexable": true, "report_reasons": null, "author": "e-rexter", "discussion_type": null, "num_comments": 5, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/1377hmo/llm_learn_personas_and_personas_can_increase/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://arxiv.org/pdf/2304.05335.pdf", "subreddit_subscribers": 2649887, "created_utc": 1683167862.0, "num_crossposts": 0, "media": null, "is_video": false}}, {"kind": "t3", "data": {"approved_at_utc": null, "subreddit": "MachineLearning", "selftext": "I am no expert at all on backpropagation. The experts may very well be able to do better with this type of butterfly neural network (as they seem to be called these days.)\n\nCode: [https://editor.p5js.org/siobhan.491/sketches/RvqZfikaE](https://editor.p5js.org/siobhan.491/sketches/RvqZfikaE)\n\nBlog reference: [https://ai462qqq.blogspot.com/2023/04/switch-net.html](https://ai462qqq.blogspot.com/2023/04/switch-net.html)", "author_fullname": "t2_9a9yn6nny", "saved": false, "mod_reason_title": null, "gilded": 0, "clicked": false, "title": "[D] Switch Net backpropagation implementation", "link_flair_richtext": [], "subreddit_name_prefixed": "r/MachineLearning", "hidden": false, "pwls": 6, "link_flair_css_class": "one", "downs": 0, "thumbnail_height": null, "top_awarded_type": null, "hide_score": false, "name": "t3_136ko72", "quarantine": false, "link_flair_text_color": "dark", "upvote_ratio": 0.6, "author_flair_background_color": null, "subreddit_type": "public", "ups": 2, "total_awards_received": 0, "media_embed": {}, "thumbnail_width": null, "author_flair_template_id": null, "is_original_content": false, "user_reports": [], "secure_media": null, "is_reddit_media_domain": false, "is_meta": false, "category": null, "secure_media_embed": {}, "link_flair_text": "Discussion", "can_mod_post": false, "score": 2, "approved_by": null, "is_created_from_ads_ui": false, "author_premium": false, "thumbnail": "self", "edited": false, "author_flair_css_class": null, "author_flair_richtext": [], "gildings": {}, "content_categories": null, "is_self": true, "mod_note": null, "created": 1683120766.0, "link_flair_type": "text", "wls": 6, "removed_by_category": null, "banned_by": null, "author_flair_type": "text", "domain": "self.MachineLearning", "allow_live_comments": false, "selftext_html": "<!-- SC_OFF --><div class=\"md\"><p>I am no expert at all on backpropagation. The experts may very well be able to do better with this type of butterfly neural network (as they seem to be called these days.)</p>\n\n<p>Code: <a href=\"https://editor.p5js.org/siobhan.491/sketches/RvqZfikaE\">https://editor.p5js.org/siobhan.491/sketches/RvqZfikaE</a></p>\n\n<p>Blog reference: <a href=\"https://ai462qqq.blogspot.com/2023/04/switch-net.html\">https://ai462qqq.blogspot.com/2023/04/switch-net.html</a></p>\n</div><!-- SC_ON -->", "likes": null, "suggested_sort": null, "banned_at_utc": null, "view_count": null, "archived": false, "no_follow": false, "is_crosspostable": true, "pinned": false, "over_18": false, "all_awardings": [], "awarders": [], "media_only": false, "can_gild": true, "spoiler": false, "locked": false, "author_flair_text": null, "treatment_tags": [], "visited": false, "removed_by": null, "num_reports": null, "distinguished": null, "subreddit_id": "t5_2r3gv", "author_is_blocked": false, "mod_reason_by": null, "removal_reason": null, "link_flair_background_color": "", "id": "136ko72", "is_robot_indexable": true, "report_reasons": null, "author": "GreenInkToThink", "discussion_type": null, "num_comments": 1, "send_replies": true, "whitelist_status": "all_ads", "contest_mode": false, "mod_reports": [], "author_patreon_flair": false, "author_flair_text_color": null, "permalink": "/r/MachineLearning/comments/136ko72/d_switch_net_backpropagation_implementation/", "parent_whitelist_status": "all_ads", "stickied": false, "url": "https://www.reddit.com/r/MachineLearning/comments/136ko72/d_switch_net_backpropagation_implementation/", "subreddit_subscribers": 2649887, "created_utc": 1683120766.0, "num_crossposts": 0, "media": null, "is_video": false}}], "before": null}}