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Mehdi Azabou
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2020 – today
- 2024
- [i12]Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L. Dyer:
GraphFM: A Scalable Framework for Multi-Graph Pretraining. CoRR abs/2407.11907 (2024) - [i11]Yizi Zhang, Yanchen Wang, Donato Jimenez-Beneto, Zixuan Wang, Mehdi Azabou, Blake A. Richards, Olivier Winter, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz:
Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. CoRR abs/2407.14668 (2024) - 2023
- [c10]Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer:
Half-Hop: A graph upsampling approach for slowing down message passing. ICML 2023: 1341-1360 - [c9]Michael J. Mendelson, Mehdi Azabou, Suma Jacob
, Nicola Grissom, David Darrow
Learning signatures of decision making from many individuals playing the same game. NER 2023: 1-5 - [c8]Carolina Urzay, Nauman Ahad, Mehdi Azabou, Aidan Schneider, Geethika Atamkuri, Keith B. Hengen, Eva L. Dyer:
Detecting change points in neural population activity with contrastive metric learning. NER 2023: 1-4 - [c7]Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer:
A Unified, Scalable Framework for Neural Population Decoding. NeurIPS 2023 - [c6]Mehdi Azabou, Michael Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay, Eva L. Dyer:
Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis. NeurIPS 2023 - [i10]Jorge Quesada
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. CoRR abs/2301.00345 (2023) - [i9]Michael J. Mendelson, Mehdi Azabou, Suma Jacob, Nicola Grissom, David Darrow, Becket Ebitz, Alexander Herman, Eva L. Dyer:
Learning signatures of decision making from many individuals playing the same game. CoRR abs/2302.11023 (2023) - [i8]Mehdi Azabou, Michael Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay, Eva L. Dyer:
Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis. CoRR abs/2303.08811 (2023) - [i7]Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer:
Half-Hop: A graph upsampling approach for slowing down message passing. CoRR abs/2308.09198 (2023) - [i6]Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael J. Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer:
A Unified, Scalable Framework for Neural Population Decoding. CoRR abs/2310.16046 (2023) - 2022
- [c5]Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko:
Large-Scale Representation Learning on Graphs via Bootstrapping. ICLR 2022 - [c4]Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer:
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. NeurIPS 2022 - [c3]Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy M. Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William R. Gray Roncal, Erik C. Johnson, Eva L. Dyer:
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. NeurIPS 2022 - [i5]Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer:
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. CoRR abs/2206.06131 (2022) - [i4]Mehdi Azabou, Michael Mendelson, Maks Sorokin, Shantanu Thakoor, Nauman Ahad, Carolina Urzay, Eva L. Dyer:
Learning Behavior Representations Through Multi-Timescale Bootstrapping. CoRR abs/2206.07041 (2022) - 2021
- [c2]Chi-Heng Lin, Mehdi Azabou, Eva L. Dyer:
Making transport more robust and interpretable by moving data through a small number of anchor points. ICML 2021: 6631-6641 - [c1]Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer:
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. NeurIPS 2021: 10587-10599 - [i3]Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu, Chi-Heng Lin, Erik C. Johnson, Kiran Bhaskaran-Nair, Max Dabagia, Keith B. Hengen
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. CoRR abs/2102.10106 (2021) - [i2]Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer:
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. CoRR abs/2111.02338 (2021) - 2020
- [i1]Chi-Heng Lin, Mehdi Azabou, Eva L. Dyer:
Making transport more robust and interpretable by moving data through a small number of anchor points. CoRR abs/2012.11589 (2020)
Coauthor Index
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