Taming the waves: sine as activation function in deep neural networks
Most deep neural networks use non-periodic and monotonic—or at least quasiconvex— activation functions. While sinusoidal activation functions have been successfully used for specific applications, they remain largely ignored and regarded as difficult to train. In this paper we formally characterize why these networks can indeed often be difficult to train even in very simple scenarios, and describe how the presence of infinitely many and shallow local minima emerges from the architecture. We also provide an explanation to the good performance achieved on a typical classification task, by showing that for several network architectures the presence of the periodic cycles is largely ignored when the learning is successful. Finally, we show that there are non-trivial tasks—such as learning algorithms—where networks using sinusoidal activations can learn faster than more established monotonic functions.
An interesting study of using Sine as activation function showing successful training of models using Sine. However the scope of tasks this is applied to is a bit too limited to be convincing. Maybe showing good results on more important tasks in addition to current toy tasks would make a stronger case?
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Taming the waves: sine as activation function in deep neural networks
Most deep neural networks use non-periodic and monotonic—or at least quasiconvex— activation functions. While sinusoidal activation functions have been successfully used for specific applications, they remain largely ignored and regarded as difficult to train. In this paper we formally characterize why these networks can indeed often be difficult to train even in very simple scenarios, and describe how the presence of infinitely many and shallow local minima emerges from the architecture. We also provide an explanation to the good performance achieved on a typical classification task, by showing that for several network architectures the presence of the periodic cycles is largely ignored when the learning is successful. Finally, we show that there are non-trivial tasks—such as learning algorithms—where networks using sinusoidal activations can learn faster than more established monotonic functions.
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>



An interesting study of using Sine as activation function showing successful training of models using Sine. However the scope of tasks this is applied to is a bit too limited to be convincing. Maybe showing good results on more important tasks in addition to current toy tasks would make a stronger case?
assistant
An interesting study of using Sine as activation function showing successful training of models using Sine. However the scope of tasks this is applied to is a bit too limited to be convincing. Maybe showing good results on more important tasks in addition to current toy tasks would make a stronger case?