| Large-scale representation learning on graphs via bootstrapping S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ... 10th International Conference on Learning Representations (ICLR 2022), 2022 | 402* | 2022 |
| Mine your own view: Self-supervised learning through across-sample prediction M Azabou, MG Azar, R Liu, CH Lin, EC Johnson, K Bhaskaran-Nair, ... NeurIPS 2021 Workshop on Self-supervised Learning: Theory and Practice, 2021 | 48 | 2021 |
| Drop, swap, and generate: A self-supervised approach for generating neural activity R Liu, M Azabou, M Dabagia, CH Lin, M Gheshlaghi Azar, K Hengen, ... Advances in neural information processing systems 34, 10587-10599, 2021 | 35* | 2021 |
| Half-Hop: A graph upsampling approach for slowing down message passing M Azabou, V Ganesh, S Thakoor, CH Lin, L Sathidevi, R Liu, M Valko, ... 40th International Conference on Machine Learning (ICML 2023) 202, 1341-1360, 2023 | 14 | 2023 |
| Making transport more robust and interpretable by moving data through a small number of anchor points CH Lin, M Azabou, EL Dyer Proceedings of machine learning research 139, 6631, 2021 | 14 | 2021 |
| Transcriptomic cell type structures in vivo neuronal activity across multiple timescales A Schneider, M Azabou, L McDougall-Vigier, DF Parks, S Ensley, ... Cell reports 42 (4), 2023 | 13 | 2023 |
| A Unified, Scalable Framework for Neural Population Decoding M Azabou, V Arora, V Ganesh, X Mao, S Nachimuthu, MJ Mendelson, ... Advances in neural information processing systems 35 (NeurIPS 2023), 2023 | 12 | 2023 |
| Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers R Liu, M Azabou, M Dabagia, J Xiao, EL Dyer Neural Information Processing Systems (NeurIPS), 2022, 2022 | 12 | 2022 |
| Learning behavior representations through multi-timescale bootstrapping M Azabou, M Mendelson, M Sorokin, S Thakoor, N Ahad, C Urzay, ... CVPR 2022, Workshop on Multi-Agent Behavior (Oral), 2022 | 3 | 2022 |
| MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction J Quesada, L Sathidevi, R Liu, N Ahad, JM Jackson, M Azabou, J Xiao, ... Advances in Neural Information Processing Systems, 2022 | 3 | 2022 |
| Using self-supervision and augmentations to build insights into neural coding M Azabou, M Dabagia, R Liu, CH Lin, KB Hengen, EL Dyer NeurIPS 2021 Workshop on Self-supervised Learning: Theory and Practice, 2021 | 2 | 2021 |
| Learning signatures of decision making from many individuals playing the same game MJ Mendelson, M Azabou, S Jacob, N Grissom, D Darrow, B Ebitz, ... 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), 1-5, 2023 | 1 | 2023 |
| Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis M Azabou, M Mendelson, N Ahad, M Sorokin, S Thakoor, C Urzay, ... Advances in neural information processing systems 35 (NeurIPS 2023 Spotlight), 2023 | 1 | 2023 |
| Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution Y Zhang, Y Wang, D Jimenez-Beneto, Z Wang, M Azabou, B Richards, ... arXiv preprint arXiv:2407.14668, 2024 | | 2024 |
| GraphFM: A Scalable Framework for Multi-Graph Pretraining D Lachi, M Azabou, V Arora, E Dyer arXiv preprint arXiv:2407.11907, 2024 | | 2024 |
| Detecting change points in neural population activity with contrastive metric learning C Urzay, N Ahad, M Azabou, A Schneider, G Atamkuri, KB Hengen, ... 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER), 1-4, 2023 | | 2023 |
Eva L. DyerGeorgia Institute of Technology在 gatech.edu 的电子邮件经过验证
