Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 7: Good paper, accept
review_content: Good short note on how one might implement bigger support convolutions using the Winograd technique. The heuristics proposed might have wider applicability. It would be great if someone figured out more general principles for automatically designing these kernels in a numerically stable way.
review_title: Useful data point on the potential of Winograd convolutions for wider filters.

final_score: rating: 7: Good paper, accept
review_content: This paper shows how Winograd convolutions can be made more numerically stable for large tile-sizes, which are more efficient. The authors show significant reduction in numerical errors and a roughly 1.4x speed increase for inception-v3, which is quite meaningful.

It is stated that "we have been able to successfully train Alexnet and Inception v3" - does this mean that the final error rate is (almost) unchanged for the network using the new convolution routines?

Given the importance of efficient convolution routines for deep learning and the solid results, I think this paper should be accepted.
review_title: Improved stability and speed for Winograd convs

Decision: Accept