Update files at 2023-12-03 11:04:59

reservoirs.html

@@ -304,7 +304,7 @@ <h1>The Impact of Noise on Recurrent Neural Networks I</h1>
     is given by a random matrix. This simplicity lends itself to intepretability.
 </p>
 <p>
-    The specific kind of reservoir computer we are going to consider are Echo State Networks (ESNs). They are a very simple
+    The specific kind of reservoir computer we are going to consider are echo state networks (ESNs). They are a very simple
     network with a few tunable parameters: the network sparsity, the bleedthrough between time steps, an encoding map, a decoding map,
     an internal transition map, and the size of the reservoir. An interesting property of ESNs is that by randomly
     initializing the weights in the encoding map and internal transition map, it is possible to learn to predict time series
@@ -315,7 +315,7 @@ <h1>The Impact of Noise on Recurrent Neural Networks I</h1>
     their size. Our analysis is more generally applicable to analog systems where the state of the system is a continuous
     value, such as the neural activations of our reservoir. What we aim to demonstrate is that in this scenario,
     noise tends to substantially impair the kinds of computations a system is able to perform.  We start with a simple
-    notebook that introduces the model (Echo State Networks), and the computational task (NARMA10).
+    notebook that introduces the model (echo state networks), and the computational task (NARMA10).
 
 
 </p>

reservoirs2.html

@@ -291,7 +291,7 @@ <h2>Research Engineer</h2>
         <article>
              <h1>The Impact of Noise on Recurrent Neural Networks II</h1>
     <div class="paragraph">
-<p> In this section, we are going to consider the simulation of the Echo State Networks discussed in the last post.
+<p> In this section, we are going to consider the simulation of the echo state networks discussed in the last post.
     This is an oddly constrained problem, and there are actually a few design decisions. Dealing with the variable
     training length is annoying. I messed around with this for a while (we will explore this in the appendix),
     but discovered that a much better thing to do is simulate an ensemble of reservoirs. In principle, each size

reservoirs3.html

@@ -292,7 +292,7 @@ <h2>Research Engineer</h2>
              <h1>The Impact of Noise on Recurrent Neural Networks III</h1>
     <div class="paragraph">
         <p> We are finally set to analyze the impacts of noise on our particular model of recurrent computation - reservoir computing
-            with Echo State Networks. In the previous post, we implemented an extremely simple noise model by simply adding Gaussian
+            with echo state networks. In the previous post, we implemented an extremely simple noise model by simply adding Gaussian
             noise to each element of the output signal from the reservoir. In principle, the dynamics of a system will have more
             complicated noise based on the details of the computation being done, but in our case using such a simple model will
             let us explore intuitively why we should expect a substantial degradation in performance in the first place. Let's dive in!