Regularization for Deep Learning: A Taxonomy
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We identify the atomic building blocks of existing methods, and decouple the assumptions they enforce from the mathematical tools they rely on. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.
 The main aim of ICLR conference, at least as it is written on its website, is to provide new results on theories, methods and algorithms, supporting further breakthroughs in AI and DL. In this respect the authors of the paper claim that their “systematic approach enables the discovery of new, improved regularization methods by combining the best properties of the existing ones.” However, the authors did not provide any discoveries concerning new approaches to regularisation supporting this claim. Thus, the main contribution of the paper is that the authors made a review and performed classification of available regularisation methods. So, the paper is in fact a survey paper, which is more appropriate for full-scale journals. The work, developed by the authors, is really big. However, I am not sure it will bring a lot of benefits for readers except those who need review for some reports, introductions in PhD thesis, etc. Although the authors mentioned some approaches to combine different regularisations, they did not performed any experiments supporting their ideas. Thus, I think that - the paper is well written in general, - it can be improved (by taking into account several important comments from the Reviewer 2) and served as a review paper in some appropriate journal, - the paper is not suited for ICLR proceedings due to reasons, mentioned above.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Regularization for Deep Learning: A Taxonomy
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a novel, systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We identify the atomic building blocks of existing methods, and decouple the assumptions they enforce from the mathematical tools they rely on. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.
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 main aim of ICLR conference, at least as it is written on its website, is to provide new results on theories, methods and algorithms, supporting further breakthroughs in AI and DL. In this respect the authors of the paper claim that their “systematic approach enables the discovery of new, improved regularization methods by combining the best properties of the existing ones.” However, the authors did not provide any discoveries concerning new approaches to regularisation supporting this claim. Thus, the main contribution of the paper is that the authors made a review and performed classification of available regularisation methods. So, the paper is in fact a survey paper, which is more appropriate for full-scale journals. The work, developed by the authors, is really big. However, I am not sure it will bring a lot of benefits for readers except those who need review for some reports, introductions in PhD thesis, etc. Although the authors mentioned some approaches to combine different regularisations, they did not performed any experiments supporting their ideas. Thus, I think that - the paper is well written in general, - it can be improved (by taking into account several important comments from the Reviewer 2) and served as a review paper in some appropriate journal, - the paper is not suited for ICLR proceedings due to reasons, mentioned above.
assistant
 The main aim of ICLR conference, at least as it is written on its website, is to provide new results on theories, methods and algorithms, supporting further breakthroughs in AI and DL. In this respect the authors of the paper claim that their “systematic approach enables the discovery of new, improved regularization methods by combining the best properties of the existing ones.” However, the authors did not provide any discoveries concerning new approaches to regularisation supporting this claim. Thus, the main contribution of the paper is that the authors made a review and performed classification of available regularisation methods. So, the paper is in fact a survey paper, which is more appropriate for full-scale journals. The work, developed by the authors, is really big. However, I am not sure it will bring a lot of benefits for readers except those who need review for some reports, introductions in PhD thesis, etc. Although the authors mentioned some approaches to combine different regularisations, they did not performed any experiments supporting their ideas. Thus, I think that - the paper is well written in general, - it can be improved (by taking into account several important comments from the Reviewer 2) and served as a review paper in some appropriate journal, - the paper is not suited for ICLR proceedings due to reasons, mentioned above.