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ORCIDEva L. Dyer
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- affiliation: Georgia Institute of Technology, Atlanta, GA, USA
- affiliation: Emory University, Atlanta, GA, USA
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Journal Articles
- 2024
[j4]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. J. Mach. Learn. Res. 25: 91:1-91:85 (2024)- 2020
- [j3]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 C. Rodgers, Eva L. Dyer, Matti S. Hämäläinen, Konrad P. Körding, Pavan Ramkumar:
Pyglmnet: Python implementation of elastic-net regularized generalized linear models. J. Open Source Softw. 5(47): 1959 (2020) - 2018
- [j2]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): 2717-2730 (2018) - 2013
- [j1]Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk:
Greedy feature selection for subspace clustering. J. Mach. Learn. Res. 14(1): 2487-2517 (2013)
Conference and Workshop Papers
- 2024
- [c27]Jingyun Xiao, Ran Liu, Eva L. Dyer:
GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings. ICLR 2024 - [c26]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. ICML 2024 - [c25]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: 2657-2667 - 2023
- [c24]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 - [c23]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: 1-5 - [c22]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 - [c21]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 - [c20]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 - 2022
- [c19]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 - [c18]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 - [c17]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 - 2021
- [c16]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: 141-145 - [c15]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 - [c14]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 - [c13]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 - [c12]Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer:
Bayesian optimization for modular black-box systems with switching costs. UAI 2021: 1024-1034 - 2020
- [c11]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: 257-266 - 2019
- [c10]Aishwarya H. Balwani, Eva L. Dyer:
Modeling Variability in Brain Architecture with Deep Feature Learning. ACSSC 2019: 1186-1191 - [c9]John Lee, Max Dabagia, Eva L. Dyer, Christopher Rozell:
Hierarchical Optimal Transport for Multimodal Distribution Alignment. NeurIPS 2019: 13453-13463 - 2018
- [c8]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: 1-4 - 2016
- [c7]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: 594-602 - [c6]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 - 2013
- [c5]Eva L. Dyer, Christoph Studer, Richard G. Baraniuk:
Subspace clustering with dense representations. ICASSP 2013: 3258-3262 - 2011
- [c4]Eva L. Dyer, Mehrdad Majzoobi, Farinaz Koushanfar:
Hybrid modeling of non-stationary process variations. DAC 2011: 194-199 - 2010
- [c3]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: 604-611 - [c2]Mehrdad Majzoobi, Eva L. Dyer, Ahmed Elnably, Farinaz Koushanfar:
Rapid FPGA delay characterization using clock synthesis and sparse sampling. ITC 2010: 457-466 - 2007
- [c1]Gregory S. Fischer, Eva L. Dyer, Csaba Csoma, Anton Deguet, Gabor Fichtinger:
Validation System of MR Image Overlay and Other Needle Insertion Techniques. MMVR 2007: 130-135
Informal and Other Publications
- 2024
- [i27]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 abs/2402.11742 (2024) - [i26]Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L. Dyer:
GraphFM: A Scalable Framework for Multi-Graph Pretraining. CoRR abs/2407.11907 (2024) - [i25]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) - [i24]Ran Liu, Wenrui Ma, Ellen L. Zippi, Hadi Pouransari, Jingyun Xiao, Christopher M. Sandino, Behrooz Mahasseni, Juri Minxha, Erdrin Azemi, Eva L. Dyer, Ali Moin:
Generalizable autoregressive modeling of time series through functional narratives. CoRR abs/2410.08421 (2024) - 2023
- [i23]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 abs/2301.00345 (2023) - [i22]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) - [i21]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) - [i20]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) - [i19]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 abs/2308.14596 (2023) - [i18]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
- [i17]Nauman Ahad, Eva L. Dyer, Keith B. Hengen, Yao Xie, Mark A. Davenport:
Learning Sinkhorn divergences for supervised change point detection. CoRR abs/2202.04000 (2022) - [i16]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) - [i15]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) - [i14]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 abs/2210.05021 (2022) - 2021
- [i13]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 abs/2102.10106 (2021) - [i12]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. CoRR abs/2109.04463 (2021) - [i11]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
- [i10]Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer:
Bayesian optimization for modular black-box systems with switching costs. CoRR abs/2006.02624 (2020) - [i9]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) - 2019
- [i8]John Lee, Max Dabagia, Eva L. Dyer, Christopher J. Rozell:
Hierarchical Optimal Transport for Multimodal Distribution Alignment. CoRR abs/1906.11768 (2019) - 2016
- [i7]Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding:
Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. CoRR abs/1602.02191 (2016) - [i6]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 abs/1604.03199 (2016) - [i5]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 abs/1604.03629 (2016) - 2015
- [i4]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 abs/1503.08169 (2015) - [i3]Eva L. Dyer, Tom Goldstein, Raajen Patel, Konrad P. Körding, Richard G. Baraniuk:
Self-Expressive Decompositions for Matrix Approximation and Clustering. CoRR abs/1505.00824 (2015) - [i2]Raajen Patel, Thomas A. Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk:
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. CoRR abs/1505.05208 (2015) - 2013
- [i1]Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk:
Greedy Feature Selection for Subspace Clustering. CoRR abs/1303.4778 (2013)
Coauthor Index
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