Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 9: Top 15% of accepted papers, strong accept
review_content: This paper provides a constructive proof to indicate that  Direct Feedback
Alignment (DFA) can be viewed as a noisy variant of a layer-wise training method called Linear Aligned Feedback Systems (LAFS). It also empirically verified that the random update matrices used in DFA are effectively readout matrices.

This work is interesting because it explained why DFA works and what is the limitation of DFA.
review_title: Interesting work

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper argues that Direct Feedback Alignment (DFA) has a very similar update rule to Linear Aligned Feedback Systems (LAFTS) which is a layer-wise training method. Part of the argument is through similarities in the update equation and they perform experiments to verify that the different part in the update rule behaves similarly in DFA and LAFST. Unfortunately, since I'm not familiar with previous work on methods similar to DFA, I don't understand why they are useful in general (I'm not convinced that being biologically plausible is an acceptable reason for ML research).
review_title: null

Decision: Accept