Discovering the mechanics of hidden neurons
Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding. This paper takes an experimental approach, with a divide-and-conquer strategy in mind: we start by studying what happens in single neurons. While being the core building block of deep neural networks, the way they encode information about the inputs and how such encodings emerge is still unknown. We report experiments providing strong evidence that hidden neurons behave like binary classifiers during training and testing. During training, analysis of the gradients reveals that a neuron separates two categories of inputs, which are impressively constant across training. During testing, we show that the fuzzy, binary partition described above embeds the core information used by the network for its prediction. These observations bring to light some of the core internal mechanics of deep neural networks, and have the potential to guide the next theoretical and practical developments.
 This paper presents an experimental study on the behavior of the units of neural networks. In particular, authors aim to show that units behave as binary classifiers during training and testing. I found the paper unnecessarily longer than the suggested 8 pages. The focus of the paper is confusing: while the introduction discusses about works on CNN model interpretability, the rest of the paper is focused on showing that each unit behaves consistently as a binary classifier, without analyzing anything in relation to interpretability. I think some formal formulation and specific examples on the relevance of the partial derivative of the loss with respect to the activation of a unit will help to understand better the main idea of the paper. Also, quantitative figures would be useful to get the big picture. For example in Figures 1 and 2 the authors show the behavior of some specific units as examples, but it would be nice to see a graph showing quantitatively the behavior of all the units at each layer. It would be also useful to see a comparison of different CNNs and see how the observation holds more or less depending on the performance of the network.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Discovering the mechanics of hidden neurons
Neural networks trained through stochastic gradient descent (SGD) have been around for more than 30 years, but they still escape our understanding. This paper takes an experimental approach, with a divide-and-conquer strategy in mind: we start by studying what happens in single neurons. While being the core building block of deep neural networks, the way they encode information about the inputs and how such encodings emerge is still unknown. We report experiments providing strong evidence that hidden neurons behave like binary classifiers during training and testing. During training, analysis of the gradients reveals that a neuron separates two categories of inputs, which are impressively constant across training. During testing, we show that the fuzzy, binary partition described above embeds the core information used by the network for its prediction. These observations bring to light some of the core internal mechanics of deep neural networks, and have the potential to guide the next theoretical and practical developments.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 This paper presents an experimental study on the behavior of the units of neural networks. In particular, authors aim to show that units behave as binary classifiers during training and testing. I found the paper unnecessarily longer than the suggested 8 pages. The focus of the paper is confusing: while the introduction discusses about works on CNN model interpretability, the rest of the paper is focused on showing that each unit behaves consistently as a binary classifier, without analyzing anything in relation to interpretability. I think some formal formulation and specific examples on the relevance of the partial derivative of the loss with respect to the activation of a unit will help to understand better the main idea of the paper. Also, quantitative figures would be useful to get the big picture. For example in Figures 1 and 2 the authors show the behavior of some specific units as examples, but it would be nice to see a graph showing quantitatively the behavior of all the units at each layer. It would be also useful to see a comparison of different CNNs and see how the observation holds more or less depending on the performance of the network.
assistant
 This paper presents an experimental study on the behavior of the units of neural networks. In particular, authors aim to show that units behave as binary classifiers during training and testing. I found the paper unnecessarily longer than the suggested 8 pages. The focus of the paper is confusing: while the introduction discusses about works on CNN model interpretability, the rest of the paper is focused on showing that each unit behaves consistently as a binary classifier, without analyzing anything in relation to interpretability. I think some formal formulation and specific examples on the relevance of the partial derivative of the loss with respect to the activation of a unit will help to understand better the main idea of the paper. Also, quantitative figures would be useful to get the big picture. For example in Figures 1 and 2 the authors show the behavior of some specific units as examples, but it would be nice to see a graph showing quantitatively the behavior of all the units at each layer. It would be also useful to see a comparison of different CNNs and see how the observation holds more or less depending on the performance of the network.