Distributed non-parametric deep and wide networks
In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). In the present work, we show that combining distributed Gaussian Processes with multi-stream deep convolutional neural networks (CNN) alleviate the need to augment a neural network with hand-crafted features. In contrast to prior work, we treat each deep neural convolutional network as an expert wherein the individual predictions (and their respective uncertainties) are combined into a Product of Experts (PoE) framework.
 - The paper is fairly written and it is clear what is being done - There is not much novelty in the paper; it combines known techniques and is a systems paper, so I would judge the contributions mainly in terms of the empirical results and messsage conveyed (see third point) - The paper builds on a previous paper (ICCV Workshops, https://arxiv.org/pdf/1707.06923.pdf), however, there is non-trivial overlap between the two papers, e.g. Fig. 1 seems to be almost the same figure from that paper, Sec 2.1 from the previous paper is largely copied - The message from the empirical validation is also not novel, in the ICCVW paper it was shown that the combination of different modalities etc. using a multiple kernel learning framework improved results (73.3 on HMDB51), while in the current paper the same message comes across with another kind of (known) method for combining different classifiers and modality (without iDT their best results are 73.6 for CNN+GP-PoE)
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Distributed non-parametric deep and wide networks
In recent work, it was shown that combining multi-kernel based support vector machines (SVMs) can lead to near state-of-the-art performance on an action recognition dataset (HMDB-51 dataset). In the present work, we show that combining distributed Gaussian Processes with multi-stream deep convolutional neural networks (CNN) alleviate the need to augment a neural network with hand-crafted features. In contrast to prior work, we treat each deep neural convolutional network as an expert wherein the individual predictions (and their respective uncertainties) are combined into a Product of Experts (PoE) framework.
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 paper is fairly written and it is clear what is being done - There is not much novelty in the paper; it combines known techniques and is a systems paper, so I would judge the contributions mainly in terms of the empirical results and messsage conveyed (see third point) - The paper builds on a previous paper (ICCV Workshops, https://arxiv.org/pdf/1707.06923.pdf), however, there is non-trivial overlap between the two papers, e.g. Fig. 1 seems to be almost the same figure from that paper, Sec 2.1 from the previous paper is largely copied - The message from the empirical validation is also not novel, in the ICCVW paper it was shown that the combination of different modalities etc. using a multiple kernel learning framework improved results (73.3 on HMDB51), while in the current paper the same message comes across with another kind of (known) method for combining different classifiers and modality (without iDT their best results are 73.6 for CNN+GP-PoE)
assistant
 - The paper is fairly written and it is clear what is being done - There is not much novelty in the paper; it combines known techniques and is a systems paper, so I would judge the contributions mainly in terms of the empirical results and messsage conveyed (see third point) - The paper builds on a previous paper (ICCV Workshops, https://arxiv.org/pdf/1707.06923.pdf), however, there is non-trivial overlap between the two papers, e.g. Fig. 1 seems to be almost the same figure from that paper, Sec 2.1 from the previous paper is largely copied - The message from the empirical validation is also not novel, in the ICCVW paper it was shown that the combination of different modalities etc. using a multiple kernel learning framework improved results (73.3 on HMDB51), while in the current paper the same message comes across with another kind of (known) method for combining different classifiers and modality (without iDT their best results are 73.6 for CNN+GP-PoE)