Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron*s input. This stochastic regularizer is comparable to nonlinearities aided by dropout, but it removes the need for a traditional nonlinearity. The connection between the GELU and the stochastic regularizer suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.
The proposed regularizer seems to be a particular combination of existing methods. Though the implied connection between nonlinearities and stochastic regularizers is intriguing, in my opinion the empirical performance does not exceed the performance achieved by similar methods by a large enough margin to arrive at a meaningful conclusion.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Bridging Nonlinearities and Stochastic Regularizers with Gaussian Error Linear Units
We propose the Gaussian Error Linear Unit (GELU), a high-performing neural network activation function. The GELU nonlinearity is the expected transformation of a stochastic regularizer which randomly applies the identity or zero map to a neuron*s input. This stochastic regularizer is comparable to nonlinearities aided by dropout, but it removes the need for a traditional nonlinearity. The connection between the GELU and the stochastic regularizer suggests a new probabilistic understanding of nonlinearities. We perform an empirical evaluation of the GELU nonlinearity against the ReLU and ELU activations and find performance improvements across all tasks.
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>



The proposed regularizer seems to be a particular combination of existing methods. Though the implied connection between nonlinearities and stochastic regularizers is intriguing, in my opinion the empirical performance does not exceed the performance achieved by similar methods by a large enough margin to arrive at a meaningful conclusion.
assistant
The proposed regularizer seems to be a particular combination of existing methods. Though the implied connection between nonlinearities and stochastic regularizers is intriguing, in my opinion the empirical performance does not exceed the performance achieved by similar methods by a large enough margin to arrive at a meaningful conclusion.