Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 4: Ok but not good enough - rejection
review_content: # Summary
This paper proposes a way to represent behavior data as 2D map and build a CNN to exploit the local information (relationship between similar topics). The experimental results show that CNN on top of the 2D representation of the topics performs better than MLPs. Although the proposed idea is interesting, the experimental result is not very convincing enough to support the main hypothesis of the paper (see below).

# Novelty
The proposed idea to represent topics into 2D map is new (to my knowledge).

# Clarity
The paper is well-written.

# Quality
The experimental result is not much convincing due to the following. 1) SCCN performs worse than LRCN which contradicts the paper's assumption that position-specific relationship is more theoretically suitable for topical metrics. 2) I think the paper should have shown learning curves on the training data. Since TDRN can be viewed as an instance of SCCN/LRCN with large convolution filters, it should be able to perform as well as SCCN/LRCN on the "training data" with a large number of hidden units. But, it may be prone to overfitting due to large number of parameters if the paper's hypothesis is correct. However, the validation/testing performance of TDRN keeps improving over epochs in the paper, which means that TDRN is underfitting and the size of TDRN is too small. In order to show that SCCN/LRCN are better than TDRN, the paper should have shown training curves and showed that TDRN is overfitting. 

# Significance
Even if the main result of the paper is correct, I am not sure that representing this specific behavior data is interesting and significant enough to be presented in the workshop. However, I am willing to withdraw this opinion if other reviewers think this is an important problem/data. 

# Pros
- The proposed method to represent behavior data in a 2D map is new.

# Cons
- The empirical result is not convincing.
- The data and problem discussed in the paper may not be significant or interesting to the community.
review_title: Review

final_score: rating: 3: Clear rejection
review_content: + Clarity: Could be much improved, although some of it may be due to my unfamiliarity with behavior studies.
  - What is the dataset used, what domain? What does the raw data represent, i.e. what are entries what are entities? There is mention of "predict the response", "organize activities into topics", and at end of Sec3 mention of data set size. Quite unclear what the problem is this paper is trying to solve.
  - Is the goal to predict future topical volume based on past topical volume? Or predict from past topical volume + neighbor current topical volume?
  - Fig1: is this for all entities, all time? (d) is for one entity, single timepoint? What is the color in Fig1(d) representing? To my best guess, in Fig1(c) neighbors now correspond to different topics, which are related across the dataset. But for a single entry, single timepoint you will either observe this map, or not. So how does this help for prediction?
  - RLE definition: what is being summed over? $\forall v \in \mathcal{V}$ -- both $v$ and $\mathcal{V}$ are not defined, and not easily inferred from context either.
  - "topical behavior" in first sentence of abstract, is not defined and I can't find it with google. I assume this refers to the fact some topic modeling is done on the behavioral data?
  - In general it seems this paper wants to be formulated broadly to be applicable to many specific instances of behavioral data, but sacrifices clarity and specificity which makes the paper almost unintelligeble.

+ Significance
  - CON: It seems this is a proprietary unnamed dataset no-one else has worked on?
  - CON: Results with CNN or LCN are only marginally better than pure LSTM, with lot of noise on test error.
  - PRO: If this work is relevant for the broad range of applications named in the first paragraph this would be quite significant. Not quite convinced if that is the case though.

+ Originality, Novelty, Quality
  - This idea to map behavioral data through topic modeling and mapping to a regular 2D Grid is probably novel.
  - At the other hand, it lacks motivation or arguments why this is even a sensible thing to do in the first place. With dimensionality reduction and mapping to a grid, most information about the original datapoints will probably be lost.

In summary: this paper would need to be thoroughly rewritten to explain (1) what is the problem we're solving, (2) how is this problem approached, and (3) intuition into the quantities introduced and motivation of the proposed approach.

review_title: Review

Decision: Reject