Review for NeurIPS paper: Characterizing emergent representations in a space of candidate learning rules for deep networks

Weaknesses: The exploration of learning rules with only two variable parameters is too restrictive. There have already been numerous learning rules proposed in the field but they do not fit into the proposed scheme. Even with two parameters characterizing feedback and Hebbian learning, a single additive mixture is only one possibility. For example, a recent development is the study of tri-factor learning rules [1-3]. There are also learning rules that deal with special cases such as hierarchical patterns that this paper studies; a family of (static) learning rules have been proposed many years ago [4].