Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 6: Marginally above acceptance threshold
review_content: The paper proposes the use of bloom filters to generate embeddings
suitable for representing very sparse data for either input or output.
The authors experiment on a set of tasks and show that they can reduce the
representation size by a factor of 5 without significant performance
reduction, and still recover calibrated probabilities.

I am not sure I understood how much time needs to be traded for space.
(maybe none? that would be good!) both for training and testing.

The experiment section is very compact, so it is hard to verify if the
baseline models are reasonable and compare to the state-of-the-art on
the selected 7 tasks (a lot of details are omitted, and I understand this
is because it has to fit in 3 pages but still).

It would have also been interesting to compare to other related approaches
(like hierarchical representations). I'm also not sure I understood how it
relates to previous attempts at using Bloom filters (like Cisse et al).
review_title: more details?

final_score: rating: 7: Good paper, accept
review_content: I quite like the idea, and I think it has some merit, but as the other reviewer mentioned it is hard to judge by the experimental section.

I think it would be good to attach a story to this idea. You are already mentioning recommender systems; in those systems being able to identify false positives is often less important than false negatives. One wouldn't want to mix up Star Wars and Finding Nemo, but a false positive on Star Wars vs Star Trek wouldn't be so bad. In this sense having non-random (but overall uniformly distributed) hashes could be a nice addition/future work.
review_title: Neat idea

Decision: Accept