Eva L. Dyer

According to our database, Eva L. Dyer authored at least 38 papers between 2007 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance.

Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer

CoRR, 2024

2023

LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration.

Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L. Dyer

CoRR, 2023

Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis.

Mehdi Azabou, Michael Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay, Eva L. Dyer

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Unified, Scalable Framework for Neural Population Decoding.

Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Detecting change points in neural population activity with contrastive metric learning.

Carolina Urzay, Nauman Ahad, Mehdi Azabou, Aidan Schneider, Geethika Atamkuri, Keith B. Hengen, Eva L. Dyer

Proceedings of the 11th International IEEE/EMBS Conference on Neural Engineering, 2023

Learning signatures of decision making from many individuals playing the same game.

Michael J. Mendelson, Mehdi Azabou, Suma Jacob, Nicola Grissom, David Darrow, Becket Ebitz, Alexander Herman, Eva L. Dyer

Proceedings of the 11th International IEEE/EMBS Conference on Neural Engineering, 2023

Half-Hop: A graph upsampling approach for slowing down message passing.

Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer

Proceedings of the International Conference on Machine Learning, 2023

2022

The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective.

Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar

CoRR, 2022

Learning Behavior Representations Through Multi-Timescale Bootstrapping.

Mehdi Azabou, Michael Mendelson, Maks Sorokin, Shantanu Thakoor, Nauman Ahad, Carolina Urzay, Eva L. Dyer

CoRR, 2022

Learning Sinkhorn divergences for supervised change point detection.

Nauman Ahad, Eva L. Dyer, Keith B. Hengen, Yao Xie, Mark A. Davenport

CoRR, 2022

MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction.

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

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers.

Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Large-Scale Representation Learning on Graphs via Bootstrapping.

Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction.

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

CoRR, 2021

Bayesian optimization for modular black-box systems with switching costs.

Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity.

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

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity.

Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Making transport more robust and interpretable by moving data through a small number of anchor points.

Chi-Heng Lin, Mehdi Azabou, Eva L. Dyer

Proceedings of the 38th International Conference on Machine Learning, 2021

Multi-Scale Modeling of Neural Structure in X-Ray Imagery.

Aishwarya H. Balwani, Joseph D. Miano, Ran Liu, Lindsey Kitchell, Judy A. Prasad, Erik C. Johnson, William R. Gray Roncal, Eva L. Dyer

Proceedings of the 2021 IEEE International Conference on Image Processing, 2021

2020

Pyglmnet: Python implementation of elastic-net regularized generalized linear models.

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 Hamalainen, Konrad P. Körding, Pavan Ramkumar

J. Open Source Softw., 2020

A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure.

Ran Liu, Cem Subakan, Aishwarya H. Balwani, Jennifer D. Whitesell, Julie Harris, Sanmi Koyejo, Eva L. Dyer

Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020

2019

Hierarchical Optimal Transport for Multimodal Distribution Alignment.

John Lee, Max Dabagia, Eva L. Dyer, Christopher Rozell

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Modeling Variability in Brain Architecture with Deep Feature Learning.

Aishwarya H. Balwani, Eva L. Dyer

Proceedings of the 53rd Asilomar Conference on Signals, Systems, and Computers, 2019

2018

RankMap: A Framework for Distributed Learning From Dense Data Sets.

Azalia Mirhoseini, Eva L. Dyer, Ebrahim M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar

IEEE Trans. Neural Networks Learn. Syst., 2018

Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data.

Theodore J. LaGrow, Michael G. Moore, Judy A. Prasad, Mark A. Davenport, Eva L. Dyer

Proceedings of the 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2018

2016

From sample to knowledge: Towards an integrated approach for neuroscience discovery.

William R. Gray Roncal, Eva L. Dyer, Doga Gürsoy, Konrad P. Körding, Narayanan Kasthuri

CoRR, 2016

Quantifying mesoscale neuroanatomy using X-ray microtomography.

Eva L. Dyer, William R. Gray Roncal, Hugo L. Fernandes, Doga Gürsoy, Xianghui Xiao, Joshua T. Vogelstein, Chris Jacobsen, Konrad P. Körding, Narayanan Kasthuri

CoRR, 2016

Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes.

Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016

Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition.

Raajen Patel, Tom Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

2015

oASIS: Adaptive Column Sampling for Kernel Matrix Approximation.

Raajen Patel, Thomas A. Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk

CoRR, 2015

RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets.

Azalia Mirhoseini, Eva L. Dyer, Ebrahim M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar

CoRR, 2015

Self-Expressive Decompositions for Matrix Approximation and Clustering.

Eva L. Dyer, Tom Goldstein, Raajen Patel, Konrad P. Körding, Richard G. Baraniuk

CoRR, 2015

2013

Greedy feature selection for subspace clustering.

Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk

J. Mach. Learn. Res., 2013

Subspace clustering with dense representations.

Eva L. Dyer, Christoph Studer, Richard G. Baraniuk

Proceedings of the IEEE International Conference on Acoustics, 2013

2011

Hybrid modeling of non-stationary process variations.

Eva L. Dyer, Mehrdad Majzoobi, Farinaz Koushanfar

Proceedings of the 48th Design Automation Conference, 2011

2010

Rapid FPGA delay characterization using clock synthesis and sparse sampling.

Mehrdad Majzoobi, Eva L. Dyer, Ahmed Elnably, Farinaz Koushanfar

Proceedings of the 2011 IEEE International Test Conference, 2010

Recovering Spikes from Noisy Neuronal Calcium Signals via Structured Sparse Approximation.

Eva L. Dyer, Marco F. Duarte, Don H. Johnson, Richard G. Baraniuk

Proceedings of the Latent Variable Analysis and Signal Separation, 2010

2007

Validation System of MR Image Overlay and Other Needle Insertion Techniques.

Gregory S. Fischer, Eva L. Dyer, Csaba Csoma, Anton Deguet, Gabor Fichtinger

Proceedings of the Medicine Meets Virtual Reality 15, 2007