Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 3: Clear rejection
review_content: The authors suggest a novel approach to classifying transcription factor (TF) binding to DNA sequences based on a neural network model that utilizes memory data structures. In the authors' experiments on a previously studied dataset, their technique exceeds the accuracy of convolutional and recurrent neural networks.

Pros:
-The authors have taken on an important problem--TF binding is critical to gene regulation and our present models are insufficient to precisely predict bound sites in large mammalian genomes. Advances in this space have great value, and the authors may have an advance here.

Major Cons:
-The most substantial problem is the authors' suggestion that the memory in their model is "dynamically learned" without any description of how that aspect is implemented. Though the authors describe how the memory is used, I'm clueless as to how the memory is specified or "learned". This is a significant absence, and a dealbreaker for my recommendation as a reviewer.
-Furthermore, the authors also decline to describe the second layer of LSTMs in the form of the g function. The authors state that the f function maps DNA sequences to a vector, and they state that the g' function shares the weights from f. The authors suggest that g is an LSTM, but fail to explain why or how the LSTM operates on a single vector.

Minor Cons:
-The authors suggest that the memory matrix column size p can take values other than 4. Doesn't this dimension refer to the 4 nucleotides? When p is set to different values, how are the authors representing the DNA sequences?
-If the authors care about producing a useful tool for biologists, they will have to relax their assumption of a dataset balanced between positive and negative examples. TFBS in mammalian genomes is picking needles out of a haystack.
-The language in the paper is sloppy in multiple places. E.g. current TFBS motifs aren't "manually" defined as the authors state; they are computationally defined using different methods. The authors also suggest that doctors may be reluctant to use their model, which is irrelevant; doctors do not examine TFBS.
review_title: Interesting direction, incomplete description

final_score: rating: 7: Good paper, accept
review_content: This work uses a softmax over a set of trained templates to predict whether or not a given protein will bind to a DNA sequence.  Given that the templates are fixed, it seems a bit of a stretch to refer to the model as a "memory" model; but the task and the results are nice.    
review_title: simple model, interesting application

Decision: Accept