Hierarchical Optimal Transport for Multimodal Distribution Alignment. - John Lee 006; Max Dabagia; Eva L. Dyer; Christopher Rozell (NeurIPS, 2019)

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. - Ran Liu; Mehdi Azabou; Max Dabagia; Chi-Heng Lin; Mohammad Gheshlaghi Azar; Keith B. Hengen; Michal Valko; Eva L. Dyer (NeurIPS, 2021)

Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. - Mehdi Azabou; Mohammad Gheshlaghi Azar; Ran Liu; Chi-Heng Lin; Erik C. Johnson; Kiran Bhaskaran-Nair; Max Dabagia; Keith B. Hengen; William R. Gray Roncal; Michal Valko; Eva L. Dyer (CoRR, 2021)

Assemblies of neurons can learn to classify well-separated distributions. - Max Dabagia; Christos H. Papadimitriou; Santosh S. Vempala (CoRR, 2021)

Assemblies of neurons learn to classify well-separated distributions. - Max Dabagia; Santosh S. Vempala; Christos H. Papadimitriou (COLT, 2022)

Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. - Ran Liu; Mehdi Azabou; Max Dabagia; Jingyun Xiao; Eva L. Dyer (NeurIPS, 2022)

Computation with Sequences in the Brain. - Max Dabagia; Christos H. Papadimitriou; Santosh S. Vempala (CoRR, 2023)

Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies. - Max Dabagia; Daniel Mitropolsky; Christos H. Papadimitriou; Santosh S. Vempala (CoRR, 2024)

Computation With Sequences of Assemblies in a Model of the Brain. - Max Dabagia; Christos H. Papadimitriou; Santosh S. Vempala (Neural Comput., 2024)