Paper:
A SIMPLE NEURAL ATTENTIVE META-LEARNER
Deep neural networks excel in regimes with large amounts of data, but tend to struggle when data is scarce or when they need to adapt quickly to changes in the task. In response, recent work in meta-learning proposes training a meta-learner on a distribution of similar tasks, in the hopes of generalization to novel but related tasks by learning a high-level strategy that captures the essence of the problem it is asked to solve. However, many recent meta-learning approaches are extensively hand-designed, either using architectures specialized to a particular application, or hard-coding algorithmic components that constrain how the meta-learner solves the task. We propose a class of simple and generic meta-learner architectures that use a novel combination of temporal convolutions and soft attention; the former to aggregate information from past experience and the latter to pinpoint specific pieces of information. In the most extensive set of meta-learning experiments to date, we evaluate the resulting Simple Neural AttentIve Learner (or SNAIL) on several heavily-benchmarked tasks. On all tasks, in both supervised and reinforcement learning, SNAIL attains state-of-the-art performance by significant margins.

Review:
The paper proposes a general neural network structure that includes TC (temporal convolution) blocks and Attention blocks for meta-learning, specifically, for episodic task learning. Through intensive experiments on various settings including few-shot image classification on Omniglot and Mini-ImageNet, and four reinforcement learning applications, the authors show that the proposed structure can achieve highly comparable performance wrt the corresponding specially designed state-of-the-art methods. The experiment results seem solid and the proposed structure is with simple design and highly generalizable. The concern is that the contribution is quite incremental from the theoretical side though it involves large amount of experimental efforts, which could be impactful. Please see the major comment below.
One major comment:
- Despite that the work is more application oriented, the paper would have been stronger and more impactful if it includes more work on the theoretical side. 
Specifically, for two folds: 
(1) in general, some more work in investigating the task space would be nice. The paper assumes the tasks are “related” or “similar” and thus transferrable; also particularly in Section 2, the authors define that the tasks follow the same distribution. But what exactly should the distribution be like to be learnable and how to quantify such “related” or “similar” relationship across tasks? 
(2) in particular, for each of the experiments that the authors conduct, it would be nice to investigate some more on when the proposed TC + Attention network would work better and thus should be used by the community; some questions to answer include: when should we prefer the proposed combination of TC + attention blocks over the other methods? The result from the paper seems to answer with “in all cases” but then that always brings the issue of “overfitting” or parameter tuning issue. I believe the paper would have been much stronger if either of the two above are further investigated.
More detailed comments:
- On Page 1, “the optimal strategy for an arbitrary range of tasks” lacks definition of “range”; also, in the setting in this paper, these tasks should share “similarity” or follow the same “distribution” and thus such “arbitrariness” is actually constrained.
- On Page 2, the notation and formulation for the meta-learning could be more mathematically rigid; the distribution over tasks is not defined. It is understandable that the authors try to make the paradigm very generalizable; but the ambiguity or the abstraction over the “task distribution” is too large to be meaningful. One suggestion would be to split into two sections, one for supervised learning and one for reinforcement learning; but both share the same design paradigm, which is generalizable.
- For results in Table 1 and Table 2, how are the confidence intervals computed? Is it over multiple runs or within the same run? It would be nice to make clear; in addition, I personally prefer either reporting raw standard deviations or conduct hypothesis testing with specified tests. The confidence intervals may not be clear without elaboration; such is also concerning in the caption for Table 3 about claiming “not statistically-significantly different” because no significance test is reported. 
- At last, some more details in implementation would be nice (package availability, run time analysis); I suppose the package or the source code would be publicly available afterwards?