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引用	213	213
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Eva L. DyerGeorgia Institute of Technology在 gatech.edu 的电子邮件经过验证
Mehdi AzabouGeorgia Institute of Technology在 gatech.edu 的电子邮件经过验证
Ran LiuGeorgia Tech在 gatech.edu 的电子邮件经过验证
Keith HengenWashington University In Saint Louis在 wustl.edu 的电子邮件经过验证
Chi-Heng LinGeorgia Institute of Technology在 gatech.edu 的电子邮件经过验证
Santosh S. VempalaGeorgia Tech在 gatech.edu 的电子邮件经过验证
Christos H PAPADIMITRIOUColumbia University在 columbia.edu 的电子邮件经过验证
Mohammad Gheshlaghi AzarCohere在 cohere.com 的电子邮件经过验证
Konrad Paul KordingNathan Mossell University Professor (PIK), bioengineering & neuroscience, UPenn, CIFAR LMB在 upenn.edu 的电子邮件经过验证
John LeeGeorgia Institute of Technology在 gatech.edu 的电子邮件经过验证
Christopher RozellJulian T. Hightower Chaired Professor of Electrical & Computer Engineering, Georgia Tech在 gatech.edu 的电子邮件经过验证
Kiran Bhaskaran-NairWashington University in St. Louis在 wustl.edu 的电子邮件经过验证
Daniel MitropolskyPhD Theoretical Computer Science, Columbia University在 cs.columbia.edu 的电子邮件经过验证

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Max Dabagia

Computer Science, Georgia Tech

在 gatech.edu 的电子邮件经过验证 - 首页

Theoretical computer science machine learning computational neuroscience


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Hierarchical optimal transport for multimodal distribution alignment J Lee, M Dabagia, E Dyer, C Rozell Advances in Neural Information Processing Systems, 13474-13484, 2019	69	2019
Mine your own view: Self-supervised learning through across-sample prediction M Azabou, MG Azar, R Liu, CH Lin, EC Johnson, K Bhaskaran-Nair, ... arXiv preprint arXiv:2102.10106, 2021	46	2021
Aligning latent representations of neural activity M Dabagia, KP Kording, EL Dyer Nature Biomedical Engineering, 1-7, 2022	36*	2022
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	26	2021
Assemblies of neurons learn to classify well-separated distributions M Dabagia, SS Vempala, C Papadimitriou Conference on Learning Theory, 3685-3717, 2022	14*	2022
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers R Liu, M Azabou, M Dabagia, J Xiao, E Dyer Advances in neural information processing systems 35, 2377-2391, 2022	12	2022
Computation with Sequences of Assemblies in a Model of the Brain M Dabagia, C Papadimitriou, S Vempala International Conference on Algorithmic Learning Theory, 499-504, 2024	7*	2024
Using self-supervision and augmentations to build insights into neural coding M Azabou, M Dabagia, R Liu, CH Lin, KB Hengen, EL Dyer Proceedings of Workshop on Self-Supervised Learning-Theory and Practice on …, 2021	2	2021
Learning with Plasticity Rules: Generalization and Robustness RC Cristian, M Dabagia, C Papadimitriou, S Vempala	1	2020
Coin-Flipping In The Brain: Statistical Learning with Neuronal Assemblies M Dabagia, D Mitropolsky, CH Papadimitriou, SS Vempala arXiv preprint arXiv:2406.07715, 2024		2024
Mine your own view: A self-supervised approach for learning representations of neural activity M Azabou, MG Azar, R Liu, CH Lin, EC Johnson, K Bhaskaran-Nair, ...

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