NeurIPS 2020

GAIT-prop: A biologically plausible learning rule derived from backpropagation of error

Meta Review

This paper presents a biologically plausible learning rule as an alternative to standard back-propagation. This is a heavily studied area in ML, with strong interest from both the ML and computational neuroscience communities. The reviewers agreed that this work presents an exciting and important contribution over the existing literature on this problem. There was extensive discussion between reviewers, with two reviewers championing the paper for acceptance. The lower scoring reviewers cited the empirical evaluation as a weakness of the paper, while others argued that the idea on its own was sufficiently interesting to the community. Ultimately, all reviewers agreed that this paper should be accepted and should generate interesting discussion at the conference.