ProxNet: End-to-End Learning of Structured Representation by Proximal Mapping
Underpinning the success of deep learning is the effective regularization that allows a broad range of structures in data to be compactly modeled in a deep architecture. Examples include transformation invariances, robustness to adversarial/random perturbations, and correlations between multiple modalities. However, most existing methods incorporate such priors either by auto-encoders, whose result is used to initialize supervised learning, or by augmenting the data with exemplifications of the transformations which, despite the improved performance of supervised learning, leaves it unclear whether the learned latent representation does encode the desired regularities. To address these issues, this work proposes an emph{end-to-end} representation learning framework that allows prior structures to be encoded emph{explicitly} in the hidden layers, and to be trained efficiently in conjunction with the supervised target. Our approach is based on proximal mapping in a reproducing kernel Hilbert space, and leverages differentiable optimization. The resulting technique is applied to generalize dropout and invariant kernel warping, and to develop novel algorithms for multiview modeling and robust temporal learning.
The authors of this paper introduce the concept of proximal mapping from sparse representation to supervised and unsupervised structures, aiming at learning structured representations. It generalize dropout and invariant kernel warping, and applied in multiview learning and sequence modelling. Generally speaking, without sufficient background knowledge, it is difficult for a reader to understand the major contributions of this paper, due to unexplained terms and notations. For example in the second paragraph of Section 2, I think x in activation functions should correspond to z in Equation (1) in some places. Thus, the authors are suggested to improve writing to clearly show the idea without too much mingling with specific techniques. Visual examples, such as Figure 2, do help. Moreover, it is unclear how structured representation is defined, and how structured representation is learned by prox-map. Finally, experimental results do support that the introducing prox-map in various neural networks improves performance.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
ProxNet: End-to-End Learning of Structured Representation by Proximal Mapping
Underpinning the success of deep learning is the effective regularization that allows a broad range of structures in data to be compactly modeled in a deep architecture. Examples include transformation invariances, robustness to adversarial/random perturbations, and correlations between multiple modalities. However, most existing methods incorporate such priors either by auto-encoders, whose result is used to initialize supervised learning, or by augmenting the data with exemplifications of the transformations which, despite the improved performance of supervised learning, leaves it unclear whether the learned latent representation does encode the desired regularities. To address these issues, this work proposes an emph{end-to-end} representation learning framework that allows prior structures to be encoded emph{explicitly} in the hidden layers, and to be trained efficiently in conjunction with the supervised target. Our approach is based on proximal mapping in a reproducing kernel Hilbert space, and leverages differentiable optimization. The resulting technique is applied to generalize dropout and invariant kernel warping, and to develop novel algorithms for multiview modeling and robust temporal learning.
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 authors of this paper introduce the concept of proximal mapping from sparse representation to supervised and unsupervised structures, aiming at learning structured representations. It generalize dropout and invariant kernel warping, and applied in multiview learning and sequence modelling. Generally speaking, without sufficient background knowledge, it is difficult for a reader to understand the major contributions of this paper, due to unexplained terms and notations. For example in the second paragraph of Section 2, I think x in activation functions should correspond to z in Equation (1) in some places. Thus, the authors are suggested to improve writing to clearly show the idea without too much mingling with specific techniques. Visual examples, such as Figure 2, do help. Moreover, it is unclear how structured representation is defined, and how structured representation is learned by prox-map. Finally, experimental results do support that the introducing prox-map in various neural networks improves performance.
assistant
The authors of this paper introduce the concept of proximal mapping from sparse representation to supervised and unsupervised structures, aiming at learning structured representations. It generalize dropout and invariant kernel warping, and applied in multiview learning and sequence modelling. Generally speaking, without sufficient background knowledge, it is difficult for a reader to understand the major contributions of this paper, due to unexplained terms and notations. For example in the second paragraph of Section 2, I think x in activation functions should correspond to z in Equation (1) in some places. Thus, the authors are suggested to improve writing to clearly show the idea without too much mingling with specific techniques. Visual examples, such as Figure 2, do help. Moreover, it is unclear how structured representation is defined, and how structured representation is learned by prox-map. Finally, experimental results do support that the introducing prox-map in various neural networks improves performance.