In light of the authors' responsiveness and the updates to the manuscript -- in particular to clarify the meta-learning task -- I am updating my score to an 8.  -----  This manuscript proposes to tackle few-shot learning with neural networks by leveraging meta-learning, a classic idea that has seen a renaissance in the last 12 months. The authors formulate few-shot learning as a sequential meta-learning problem: each "example" includes a sequence of batches of "training" pairs, followed by a final "test" batch. The inputs at each "step" include the outputs of a "base learner" (e.g., training loss and gradients), as well as the base learner's current state (parameters). The paper applies an LSTM to this meta-learning problem, using the inner memory cells in the *second* layer to directly model the updated parameters of the base learner. In doing this, they note similarities between the respective update rules of LSTM memory cells and gradient descent. Updates to the LSTM meta-learner are computed based on the base learner's prediction loss for the final "test" batch. The authors make several simplifying assumptions, such as sharing weights across all second layer cells (analogous to using the same learning rate for all parameters). The paper recreates the Mini-ImageNet data set proposed in Vinyals et al 2016, and shows that the meta-learner LSTM is competitive with the current state-of-the-art (Matchin Networks, Vinyals 2016) on 1- and 5-shot learning.  Strengths: - It is intriguing -- and in hindsight, natural -- to cast the few-shot learning problem as a sequential (meta-)learning problem. While the authors did not originate the general idea of persisting learning across a series of learning problems, I think it is fair to say that they have advanced the state of the art, though I cannot confidently assert its novelty as I am not deeply familiar with recent work on meta-learning. - The proposed approach is competitive with and outperforms Vinyals 2016 in 1-shot and 5-shot Mini-ImageNet experiments. - The base learner in this setting (simple ConvNet classifier) is quite different from the nearest-neighbor-on-top-of-learned-embedding approach used in Vinyals 2016. It is always exciting when state-of-the-art results can be reported using very different approaches, rather than incremental follow-up work. - As far as I know, the insight about the relationship between the memory cell and gradient descent updates is novel here. It is interesting regardless. - The paper offers several practical insights about how to design and train an LSTM meta-learner, which should make it easier for others to replicate this work and apply these ideas to new problems. These include proper initialization, weight sharing across coordinates, and the importance of normalizing/rescaling the loss, gradient, and parameter inputs. Some of the insights have been previously described (the importance of simulating test conditions during meta-training; assuming independence between meta-learner and base learner parameters when taking gradients with respect to the meta-learner parameters), but the discussion here is useful nonetheless.  Weaknesses: - The writing is at times quite opaque. While it describes very interesting work, I would not call the paper an enjoyable read. It took me multiple passes (as well as consulting related work) to understand the general learning problem. The task description in Section 2 (Page 2) is very abstract and uses notation and language that is not common outside of this sub-area. The paper could benefit from a brief concrete example (based on MNIST is fine), perhaps paired with a diagram illustrating a sequence of few-shot learning tasks. This would definitely make it accessible to a wider audience. - Following up on that note, the precise nature of the N-class, few-shot learning problem here is unclear to me. Specifically, the Mini-ImageNet data set has 100 labels, of which 64/16/20 are used during meta-training/validation/testing. Does this mean that only 64/100 classes are observed through meta-training? Or does it mean that only 64/100 are observed in each batch, but on average all 100 are observed during meta-training? If it's the former, how many outputs does the softmax layer of the ConvNet base learner have during meta-training? 64 (only those observed in training) or 100 (of which 36 are never observed)? Many other details like these are unclear (see question). - The plots in Figure 2 are pretty uninformative in and of themselves, and the discussion section offers very little insight around them.  This is an interesting paper with convincing results. It seems like a fairly clear accept, but the presentation of the ideas and work therein could be improved. I will definitely raise my score if the writing is improved.
Output: en