Nonparametrically Learning Activation Functions in Deep Neural Nets
We provide a principled framework for nonparametrically learning activation functions in deep neural networks. Currently, state-of-the-art deep networks treat choice of activation function as a hyper-parameter before training. By allowing activation functions to be estimated as part of the training procedure, we expand the class of functions that each node in the network can learn. We also provide a theoretical justification for our choice of nonparametric activation functions and demonstrate that networks with our nonparametric activation functions generalize well. To demonstrate the power of our novel techniques, we test them on image recognition datasets and achieve up to a 15% relative increase in test performance compared to the baseline.
This paper provides a principled framework for nonparametrically learning activation functions in deep neural networks. A theoretical justification for authors* choice of nonparametric activation functions is given. Theoretical results are satisfactory but I particularly like the experimental setup where their methods are tested on image recognition datasets and achieve up to a 15% relative increase in test performance compared to the baseline. Well-written paper and novel theoretical techniques. The intuition behind the proof of Theorem 4.7 can be given in a little bit more clear way in the main body of the paper, but the Appendix clarifies everything.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Nonparametrically Learning Activation Functions in Deep Neural Nets
We provide a principled framework for nonparametrically learning activation functions in deep neural networks. Currently, state-of-the-art deep networks treat choice of activation function as a hyper-parameter before training. By allowing activation functions to be estimated as part of the training procedure, we expand the class of functions that each node in the network can learn. We also provide a theoretical justification for our choice of nonparametric activation functions and demonstrate that networks with our nonparametric activation functions generalize well. To demonstrate the power of our novel techniques, we test them on image recognition datasets and achieve up to a 15% relative increase in test performance compared to the baseline.
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 provides a principled framework for nonparametrically learning activation functions in deep neural networks. A theoretical justification for authors* choice of nonparametric activation functions is given. Theoretical results are satisfactory but I particularly like the experimental setup where their methods are tested on image recognition datasets and achieve up to a 15% relative increase in test performance compared to the baseline. Well-written paper and novel theoretical techniques. The intuition behind the proof of Theorem 4.7 can be given in a little bit more clear way in the main body of the paper, but the Appendix clarifies everything.
assistant
This paper provides a principled framework for nonparametrically learning activation functions in deep neural networks. A theoretical justification for authors* choice of nonparametric activation functions is given. Theoretical results are satisfactory but I particularly like the experimental setup where their methods are tested on image recognition datasets and achieve up to a 15% relative increase in test performance compared to the baseline. Well-written paper and novel theoretical techniques. The intuition behind the proof of Theorem 4.7 can be given in a little bit more clear way in the main body of the paper, but the Appendix clarifies everything.