Review for NeurIPS paper: Modern Hopfield Networks and Attention for Immune Repertoire Classification

Weaknesses: This manuscript contains a highly theoretical analysis of modern Hopfield networks and their relationship to the attention mechanism of a transformer model. It also contains a deep model that addresses the machine learning task of immune repertoire classification. The major issue with this submission is that the connection between the two topics addressed in this paper, (i) classification of immune repertoires, and (ii) equivalence of the update rule of modern Hopfield networks and the attention mechanism of the transformer, is at best unclear. It feels as though two distinct papers have been condensed into one. Overall, combining these two results into one paper results in a main text manuscript that does not provide sufficient detail about either.