Venue: NeurIPS 2019 Workshop Neuro_AI

Reviews:

final_score: rating: null
review_content: importance_comment: New strategies for learning that use more biologically-plausible learning rules are of extreme importance for the field.
rigor_comment: Results appear to be sound.
clarity_comment: Well written.
intersection_comment: neural-inspired learning
comment: Overall comments:  Well written paper that explores an interesting idea. The material presented is novel and relevant to the workshop. Experiments conducted do a good job of supporting the authors' claims. 

Several small typos:
Line  7   –  “but   is  still”   instead   of   “but   still”  and   “small   perturbations   of   magnitude”   instead   of   “small perturbation magnitude”
Line 34 – Interchange the order of “fa” and “bp” 
Line 52 – Kurakin et al
Line 53 – Replace “change” with “changes”
Replace BMI with BIM wherever appropriate.
review_title: Biologically-plausible learning without weight transport provides robustness to attacks

final_score: rating: null
review_content: importance_comment: This work might open up a new class of neural network model learning framework that could go beyond simply solving adversarial attacks. 
rigor_comment: It is hard to judge the rigor with such little information. Overall, it seems pretty well managed. 
clarity_comment: The document has been well written. 
intersection_comment: The work is inspired by a critical difference in feedback connection as applicable to the brain and the models. The author are putting forward a very interesting proposition and it is worth discussing further.
review_title: Interesting idea -- but need more robust testing

final_score: rating: null
review_content: importance_comment: Premise is that feedback alignment networks are also more robust to adversarial attacks. The authors show  because the "gradient" in the feedback pathway is a rough approximation, it is hard to use this gradient to train an adversarial attack.

The basic premise is very strange. Adversarial attacks are artificial: attacker has access to gradient of the loss function. For FA networks, it's unclear why an attacker could not access true gradient, and be forced to use the approximate gradient.
rigor_comment: Overall the technical aspects of this paper seem sound. 
clarity_comment: No trouble understanding the material or writing
intersection_comment: By focusing on the more biologically plausible "feedback alignment" networks, the paper does sit at the intersection of neuro and AI. However at present, adversarial attacks likely have much larger relevance to AI than neuro.
comment: The premise of the work must be clarified. As well as whether or how adversarial attacks (as framed) might have relevance to neuroscience.
review_title: Sound but premise is strange/unexplained

Decision: Accept (Poster)