Exploring the Application of Deep Learning for Supervised Learning Problems
One of the main difficulties in applying deep neural nets (DNNs) to new domains is the need to explore multiple architectures in order to discover ones that perform well. We analyze a large set of DNNs across multiple domains and derive insights regarding their effectiveness. We also analyze the characteristics of various DNNs and the general effect they may have on performance. Finally, we explore the application of meta-learning to the problem of architecture ranking. We demonstrate that by using topological features and modeling the changes in its weights, biases and activation functions layers of the initial training steps, we are able to rank architectures based on their predicted performance. We consider this work to be a first step in the important and challenging direction of exploring the space of different neural network architectures.
This paper presents an intriguing study of how one can pose architecture search as a meta learning problem. By collecting features from networks trained on various datasets and training a “ranking classifier” (the actual details of the classifier do not seem to be described in detail) one can potentially infer what a good architecture for a new problem could be by simply running the ranker on the extracted features for a new problem setup. One notable comment from the paper is that the authors fix some important hyper-parameters for all the networks. I am of the opinion that optimizing the learning rate (and its decay schedule) is actually quite important. I hypothesize that a lot of the conclusions of this paper may change quite a bit if the authors did an actual search over the rates instead. I suspect that instead of training 11k nets, one can train 2k nets with 5 learning rates each and get a much better result that is actually compelling. I am not convinced that the protocol for generating the various architectures is doing a good job at creating a diversity of architecture (simply because of the max depth of 8 layers and 14 components overall). I suspect that most of these generated architectures are actually almost identical performance-wise and that it’s a waste to train so many of them on so many tasks. Unless the authors are already doing this, they should define a pruning mechanism that filters out nets that are too similar to already existing ones. The batch normalization experiments in Table 2 seem odd and under-explained. It is also well-known that the optimal learning rates when using batch norm vs. not using batch norm can differ by an order of magnitude so given the fixed learning rate throughout all experiments, I take these results with some grain of salt. I am not sure we got many insights into the kinds of architectures that ended up being at the top. Either visualizations, or trends (or both), would be great. This work seems to conflate the study of parallel vs. serial architectures with the study of meta learning, which are somewhat distinct issues. I take issue with the table that compares parallel vs. serial performance (table 2) simply because the right way would be to filter the architectures by the same number of parameters / capacity. Ultimately the conclusion seems to be that when applying deep nets in a new domain, it is difficult to come up with a good architecture in advance. In that sense, it is hard to see the paper as a constructive result, because it’s conclusions are that while the ranker may do a good job often-times, it’s not that reliable. Thus I am not convinced that this particular result will be of practical use to folks who are intending to use deep nets for a new domain.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Exploring the Application of Deep Learning for Supervised Learning Problems
One of the main difficulties in applying deep neural nets (DNNs) to new domains is the need to explore multiple architectures in order to discover ones that perform well. We analyze a large set of DNNs across multiple domains and derive insights regarding their effectiveness. We also analyze the characteristics of various DNNs and the general effect they may have on performance. Finally, we explore the application of meta-learning to the problem of architecture ranking. We demonstrate that by using topological features and modeling the changes in its weights, biases and activation functions layers of the initial training steps, we are able to rank architectures based on their predicted performance. We consider this work to be a first step in the important and challenging direction of exploring the space of different neural network architectures.
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>



This paper presents an intriguing study of how one can pose architecture search as a meta learning problem. By collecting features from networks trained on various datasets and training a “ranking classifier” (the actual details of the classifier do not seem to be described in detail) one can potentially infer what a good architecture for a new problem could be by simply running the ranker on the extracted features for a new problem setup. One notable comment from the paper is that the authors fix some important hyper-parameters for all the networks. I am of the opinion that optimizing the learning rate (and its decay schedule) is actually quite important. I hypothesize that a lot of the conclusions of this paper may change quite a bit if the authors did an actual search over the rates instead. I suspect that instead of training 11k nets, one can train 2k nets with 5 learning rates each and get a much better result that is actually compelling. I am not convinced that the protocol for generating the various architectures is doing a good job at creating a diversity of architecture (simply because of the max depth of 8 layers and 14 components overall). I suspect that most of these generated architectures are actually almost identical performance-wise and that it’s a waste to train so many of them on so many tasks. Unless the authors are already doing this, they should define a pruning mechanism that filters out nets that are too similar to already existing ones. The batch normalization experiments in Table 2 seem odd and under-explained. It is also well-known that the optimal learning rates when using batch norm vs. not using batch norm can differ by an order of magnitude so given the fixed learning rate throughout all experiments, I take these results with some grain of salt. I am not sure we got many insights into the kinds of architectures that ended up being at the top. Either visualizations, or trends (or both), would be great. This work seems to conflate the study of parallel vs. serial architectures with the study of meta learning, which are somewhat distinct issues. I take issue with the table that compares parallel vs. serial performance (table 2) simply because the right way would be to filter the architectures by the same number of parameters / capacity. Ultimately the conclusion seems to be that when applying deep nets in a new domain, it is difficult to come up with a good architecture in advance. In that sense, it is hard to see the paper as a constructive result, because it’s conclusions are that while the ranker may do a good job often-times, it’s not that reliable. Thus I am not convinced that this particular result will be of practical use to folks who are intending to use deep nets for a new domain.
assistant
This paper presents an intriguing study of how one can pose architecture search as a meta learning problem. By collecting features from networks trained on various datasets and training a “ranking classifier” (the actual details of the classifier do not seem to be described in detail) one can potentially infer what a good architecture for a new problem could be by simply running the ranker on the extracted features for a new problem setup. One notable comment from the paper is that the authors fix some important hyper-parameters for all the networks. I am of the opinion that optimizing the learning rate (and its decay schedule) is actually quite important. I hypothesize that a lot of the conclusions of this paper may change quite a bit if the authors did an actual search over the rates instead. I suspect that instead of training 11k nets, one can train 2k nets with 5 learning rates each and get a much better result that is actually compelling. I am not convinced that the protocol for generating the various architectures is doing a good job at creating a diversity of architecture (simply because of the max depth of 8 layers and 14 components overall). I suspect that most of these generated architectures are actually almost identical performance-wise and that it’s a waste to train so many of them on so many tasks. Unless the authors are already doing this, they should define a pruning mechanism that filters out nets that are too similar to already existing ones. The batch normalization experiments in Table 2 seem odd and under-explained. It is also well-known that the optimal learning rates when using batch norm vs. not using batch norm can differ by an order of magnitude so given the fixed learning rate throughout all experiments, I take these results with some grain of salt. I am not sure we got many insights into the kinds of architectures that ended up being at the top. Either visualizations, or trends (or both), would be great. This work seems to conflate the study of parallel vs. serial architectures with the study of meta learning, which are somewhat distinct issues. I take issue with the table that compares parallel vs. serial performance (table 2) simply because the right way would be to filter the architectures by the same number of parameters / capacity. Ultimately the conclusion seems to be that when applying deep nets in a new domain, it is difficult to come up with a good architecture in advance. In that sense, it is hard to see the paper as a constructive result, because it’s conclusions are that while the ranker may do a good job often-times, it’s not that reliable. Thus I am not convinced that this particular result will be of practical use to folks who are intending to use deep nets for a new domain.