Eva L. Dyer

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Mehdi AzabouColumbia UniversityVerified email at columbia.edu
Konrad Paul KordingNathan Mossell University Professor (PIK), bioengineering & neuroscience, UPenn, CIFAR LMBVerified email at upenn.edu
Mohammad Gheshlaghi AzarCohereVerified email at cohere.com
Richard BaraniukC. Sidney Burrus Professor, Electrical & Computer Engineering, Rice UniversityVerified email at rice.edu
Ran LiuApple AIML, Georgia TechVerified email at gatech.edu
Michal ValkoChief Models Officer @ Stealth Startup, Inria & MVA - Ex: Llama at Meta; Gemini and BYOL @ DeepmindVerified email at meta.com
Shantanu ThakoorResearch Engineer at DeepMindVerified email at google.com
Keith HengenWashington University In Saint LouisVerified email at wustl.edu
Chi-Heng LinSamsung Research America || Georgia Institute of TechnologyVerified email at gatech.edu
William R. Gray-Roncal, Ph.D.Johns Hopkins University Applied Physics LaboratoryVerified email at jhu.edu
Doğa GürsoyArgonne National LaboratoryVerified email at anl.gov
Max DabagiaComputer Science, Georgia TechVerified email at gatech.edu
Aishwarya BalwaniGeorgia Institute of TechnologyVerified email at gatech.edu
Petar VeličkovićSenior Staff Research Scientist, Google DeepMind | Affiliated Lecturer, University of CambridgeVerified email at google.com
Judy A PrasadUniversity of North Carolina at Chapel Hill | KickStart Venture ServicesVerified email at unc.edu
Vincent De AndradeArgonne National LaboratoryVerified email at anl.gov
Rémi MunosFAIR, MetaVerified email at inria.fr
Lee E. MillerProfessor of Neuroscience, Physical Medicine and Rehabilitation, Biomedical EngineeringVerified email at northwestern.edu
Aswin C SankaranarayananProfessor, ECE, Carnegie Mellon UniversityVerified email at andrew.cmu.edu
Chethan PandarinathEmory University and Georgia Tech, Meta Reality LabsVerified email at emory.edu

Eva L. Dyer

Georgia Institute of Technology

Verified email at gatech.edu - Homepage

Computational Neuroscience Machine Learning Signal Processing Self-Supervised Learning


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Large-scale representation learning on graphs via bootstrapping S Thakoor, C Tallec, MG Azar, M Azabou, EL Dyer, R Munos, P Veličković, ... International Conference on Learning Representations (ICLR), 2022	520*	2022
Greedy feature selection for subspace clustering EL Dyer, AC Sankaranarayanan, RG Baraniuk Journal of Machine Learning Research 14 (1), 2487-2517, 2013	209	2013
Latent factors and dynamics in motor cortex and their application to brain–machine interfaces C Pandarinath, KC Ames, AA Russo, A Farshchian, LE Miller, EL Dyer, ... Journal of Neuroscience 38 (44), 9390-9401, 2018	114	2018
Low-dose X-ray tomography through a deep convolutional neural network X Yang, V De Andrade, W Scullin, EL Dyer, N Kasthuri, F De Carlo, ... Scientific Reports 8 (1), 1-13, 2018	104	2018
Quantifying mesoscale neuroanatomy using X-ray microtomography EL Dyer, WG Roncal, JA Prasad, HL Fernandes, D Gürsoy, V De Andrade, ... eNeuro 4 (5), 2017	88	2017
Neural Latents Benchmark'21: Evaluating latent variable models of neural population activity F Pei, J Ye, D Zoltowski, A Wu, RH Chowdhury, H Sohn, JE O'Doherty, ... Advances in Neural Information Processing Systems 34, Datasets and …, 2021	86	2021
Hierarchical optimal transport for multimodal distribution alignment J Lee, M Dabagia, EL Dyer, C Rozell Advances in Neural Information Processing Systems 32, 2019	80	2019
A cryptography-based approach for movement decoding EL Dyer, M Gheshlaghi Azar, MG Perich, HL Fernandes, S Naufel, ... Nature Biomedical Engineering 1 (12), 967-976, 2017	64	2017
De novo evolution of macroscopic multicellularity GO Bozdag, SA Zamani-Dahaj, TC Day, PC Kahn, AJ Burnetti, DT Lac, ... Nature, pp.1-8, 2023	57	2023
Mine your own view: Self-supervised learning through across-sample prediction M Azabou, MG Azar, R Liu, CH Lin, EC Johnson, K Bhaskaran-Nair, ... Advances in Neural Information Processing Systems (NeurIPS) Workshop on Self …, 2021	54	2021
Aligning latent representations of neural activity M Dabagia, KP Kording, EL Dyer Nature Biomedical Engineering, 2023	42*	2023
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	41*	2021
Rapid FPGA characterization using clock synthesis and signal sparsity M Majzoobi, EL Dyer, A Elnably, F Koushanfar IEEE International Test Conference (ITC), 2010	40*	2010
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 (NeurIPS) 2023, 2023	30	2023
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, ... International Conference on Machine Learning (ICML), 2023	26	2023
Pyglmnet: Python implementation of elastic-net regularized generalized linear models M Jas, T Achakulvisut, A Idrizović, D Acuna, M Antalek, V Marques, ... Journal of Open Source Software 5 (47), 2020	23	2020
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective CH Lin, C Kaushik, EL Dyer, V Muthukumar Journal of Machine Learning Research (JMLR), 2024	22	2024
Making transport more robust and interpretable by moving data through a small number of anchor points CH Lin, M Azabou, EL Dyer International Conference on Machine Learning (ICML) 139, 6631, 2021	20	2021
A three-dimensional thalamocortical dataset for characterizing brain heterogeneity JA Prasad, AH Balwani, EC Johnson, JD Miano, V Sampathkumar, ... Scientific Data 7 (1), 1-7, 2020	19	2020
Self-expressive decompositions for matrix approximation and clustering EL Dyer, TA Goldstein, R Patel, KP Kording, RG Baraniuk arXiv preprint arXiv:1505.00824, 2015	19	2015

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