Eva L. Dyer

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 48

Top Topics

Distributed Learning

Matrix Approximation

Switching Costs

Top Venues

Publications

Jingyun Xiao, Ran Liu, Eva L. Dyer
GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings. ICLR (2024)
Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L. Dyer
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration. WACV (2024)
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 (2024)
Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L. Dyer
GraphFM: A Scalable Framework for Multi-Graph Pretraining. CoRR (2024)
Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. CoRR (2024)
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)
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 (2023)
Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L. Dyer
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration. CoRR (2023)
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 (2023)
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)
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 (2023)
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)
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. NER (2023)
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)
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. CoRR (2023)
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 (2023)
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)
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 (2022)
Nauman Ahad, Eva L. Dyer, Keith B. Hengen, Yao Xie, Mark A. Davenport
Learning Sinkhorn divergences for supervised change point detection. CoRR (2022)
Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. CoRR (2022)
Mehdi Azabou, Michael Mendelson, Maks Sorokin, Shantanu Thakoor, Nauman Ahad, Carolina Urzay, Eva L. Dyer
Learning Behavior Representations Through Multi-Timescale Bootstrapping. CoRR (2022)
Jorge Quesada, Lakshmi Sathidevi, Ran Liu, Nauman Ahad, Joy M. Jackson, Mehdi Azabou, Jingyun Xiao, Christopher Liding, Matthew Jin, Carolina Urzay, William Gray-Roncal, Erik Johnson, Eva L. Dyer
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. NeurIPS (2022)
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)
Felix Pei, Joel Ye, David M. Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O'Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark M. Churchland, Mehrdad Jazayeri, Lee E. Miller, Jonathan W. Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. NeurIPS Datasets and Benchmarks (2021)
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 (2021)
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)
Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer
Bayesian optimization for modular black-box systems with switching costs. UAI (2021)
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
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. CoRR (2021)
Felix Pei, Joel Ye, David L. Zoltowski, Anqi Wu, Raeed H. Chowdhury, Hansem Sohn, Joseph E. O'Doherty, Krishna V. Shenoy, Matthew T. Kaufman, Mark M. Churchland, Mehrdad Jazayeri, Lee E. Miller, Jonathan W. Pillow, Il Memming Park, Eva L. Dyer, Chethan Pandarinath
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. CoRR (2021)
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)
Aishwarya H. Balwani, Joseph D. Miano, Ran Liu, Lindsey Kitchell, Judy A. Prasad, Erik C. Johnson, William R. Gray Roncal, Eva L. Dyer
Multi-Scale Modeling of Neural Structure in X-Ray Imagery. ICIP (2021)
Ran Liu, Cem Subakan, Aishwarya H. Balwani, Jennifer D. Whitesell, Julie Harris, Sanmi Koyejo, Eva L. Dyer
A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure. MICCAI (5) (2020)
Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer
Bayesian optimization for modular black-box systems with switching costs. CoRR (2020)
Mainak Jas, Titipat Achakulvisut, Aid Idrizovic, Daniel E. Acuna, Matthew Antalek, Vinícius Marques, Tommy Odland, Ravi Garg, Mayank Agrawal, Yu Umegaki, Peter Foley, Hugo Fernandes, Drew Harris, Beibin Li, Olivier Pieters, Scott Otterson, Giovanni De Toni, Chris Rodgers, Eva L. Dyer, Matti Hamalainen, Konrad P. Körding, Pavan Ramkumar
Pyglmnet: Python implementation of elastic-net regularized generalized linear models. J. Open Source Softw. 5 (47) (2020)
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 (2020)
Aishwarya H. Balwani, Eva L. Dyer
Modeling Variability in Brain Architecture with Deep Feature Learning. ACSSC (2019)
John Lee, Max Dabagia, Eva L. Dyer, Christopher J. Rozell
Hierarchical Optimal Transport for Multimodal Distribution Alignment. CoRR (2019)
John Lee, Max Dabagia, Eva L. Dyer, Christopher Rozell
Hierarchical Optimal Transport for Multimodal Distribution Alignment. NeurIPS (2019)
Theodore J. LaGrow, Michael G. Moore, Judy A. Prasad, Mark A. Davenport, Eva L. Dyer
Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data. EMBC (2018)
Azalia Mirhoseini, Eva L. Dyer, Ebrahim M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar
RankMap: A Framework for Distributed Learning From Dense Data Sets. IEEE Trans. Neural Networks Learn. Syst. 29 (7) (2018)
Eva L. Dyer, William R. Gray Roncal, Hugo L. Fernandes, Doga Gürsoy, Xianghui Xiao, Joshua T. Vogelstein, Chris Jacobsen, Konrad P. Körding, Narayanan Kasthuri
Quantifying mesoscale neuroanatomy using X-ray microtomography. CoRR (2016)
Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. UAI (2016)
Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. CoRR (2016)
Raajen Patel, Tom Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk
Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition. SDM (2016)
William R. Gray Roncal, Eva L. Dyer, Doga Gürsoy, Konrad P. Körding, Narayanan Kasthuri
From sample to knowledge: Towards an integrated approach for neuroscience discovery. CoRR (2016)
Eva L. Dyer, Tom Goldstein, Raajen Patel, Konrad P. Körding, Richard G. Baraniuk
Self-Expressive Decompositions for Matrix Approximation and Clustering. CoRR (2015)
Azalia Mirhoseini, Eva L. Dyer, Ebrahim M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar
RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets. CoRR (2015)
Raajen Patel, Thomas A. Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. CoRR (2015)
Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk
Greedy feature selection for subspace clustering. J. Mach. Learn. Res. 14 (1) (2013)
Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk
Greedy Feature Selection for Subspace Clustering CoRR (2013)
Eva L. Dyer, Christoph Studer, Richard G. Baraniuk
Subspace clustering with dense representations. ICASSP (2013)
Eva L. Dyer, Mehrdad Majzoobi, Farinaz Koushanfar
Hybrid modeling of non-stationary process variations. DAC (2011)
Eva L. Dyer, Marco F. Duarte, Don H. Johnson, Richard G. Baraniuk
Recovering Spikes from Noisy Neuronal Calcium Signals via Structured Sparse Approximation. LVA/ICA (2010)
Mehrdad Majzoobi, Eva L. Dyer, Ahmed Elnably, Farinaz Koushanfar
Rapid FPGA delay characterization using clock synthesis and sparse sampling. ITC (2010)