Validation System of MR Image Overlay and Other Needle Insertion Techniques. - Gregory S. Fischer; Eva L. Dyer; Csaba Csoma; Anton Deguet; Gabor Fichtinger (MMVR, 2007)

Recovering Spikes from Noisy Neuronal Calcium Signals via Structured Sparse Approximation. - Eva L. Dyer; Marco F. Duarte; Don H. Johnson; Richard G. Baraniuk (LVA/ICA, 2010)

Rapid FPGA delay characterization using clock synthesis and sparse sampling. - Mehrdad Majzoobi; Eva L. Dyer; Ahmed Elnably; Farinaz Koushanfar (ITC, 2010)

Hybrid modeling of non-stationary process variations. - Eva L. Dyer; Mehrdad Majzoobi; Farinaz Koushanfar (DAC, 2011)

Subspace clustering with dense representations. - Eva L. Dyer; Christoph Studer; Richard G. Baraniuk (ICASSP, 2013)

Greedy feature selection for subspace clustering. - Eva L. Dyer; Aswin C. Sankaranarayanan; Richard G. Baraniuk (J. Mach. Learn. Res., 2013)

Self-Expressive Decompositions for Matrix Approximation and Clustering. - Eva L. Dyer; Tom Goldstein; Raajen Patel; Konrad P. Körding; 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)

oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. - Raajen Patel; Thomas A. Goldstein; Eva L. Dyer; Azalia Mirhoseini; Richard G. Baraniuk (CoRR, 2015)

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 (SDM, 2016)

Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. - Mohammad Gheshlaghi Azar; Eva L. Dyer; Konrad P. Körding (UAI, 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)

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)

Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data. - Theodore J. LaGrow; Michael G. Moore; Judy A. Prasad; Mark A. Davenport; Eva L. Dyer (EMBC, 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)

Modeling Variability in Brain Architecture with Deep Feature Learning. - Aishwarya H. Balwani; Eva L. Dyer (ACSSC, 2019)

Hierarchical Optimal Transport for Multimodal Distribution Alignment. - John Lee 006; Max Dabagia; Eva L. Dyer; Christopher Rozell (NeurIPS, 2019)

A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure. - Ran Liu; Cem Subakan; Aishwarya H. Balwani; Jennifer D. Whitesell; Julie Harris 002; Sanmi Koyejo; Eva L. Dyer (MICCAI (5), 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; et al. (J. Open Source Softw., 2020)

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 (ICIP, 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 (ICML, 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 (NeurIPS, 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; et al. (NeurIPS Datasets and Benchmarks, 2021)

Bayesian optimization for modular black-box systems with switching costs. - Chi-Heng Lin; Joseph D. Miano; Eva L. Dyer (UAI, 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)

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 (ICLR, 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 (NeurIPS, 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; et al. (NeurIPS, 2022)

Learning Sinkhorn divergences for supervised change point detection. - Nauman Ahad; Eva L. Dyer; Keith B. Hengen; Yao Xie; Mark A. Davenport (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)

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 (ICML, 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 (NER, 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 (NER, 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 (NeurIPS, 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 (NeurIPS, 2023)

GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings. - Jingyun Xiao; Ran Liu; Eva L. Dyer (ICLR, 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 (ICML, 2024)

Your contrastive learning problem is secretly a distribution alignment problem. - Zihao Chen; Chi-Heng Lin; Ran Liu; Jingyun Xiao; Eva L. Dyer (NeurIPS, 2024)

Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. - Yizi Zhang; Yanchen Wang; Donato Jiménez-Benetó; Zixuan Wang; Mehdi Azabou; Blake A. Richards; Renee Tung; Olivier Winter; International Brain Laboratory; Eva L. Dyer; Liam Paninski; Cole L. Hurwitz (NeurIPS, 2024)

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 (WACV, 2024)

GraphFM: A Scalable Framework for Multi-Graph Pretraining. - Divyansha Lachi; Mehdi Azabou; Vinam Arora; Eva L. Dyer (CoRR, 2024)

Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. - Yizi Zhang; Yanchen Wang; Donato Jiménez-Benetó; Zixuan Wang; Mehdi Azabou; Blake A. Richards; Olivier Winter; International Brain Laboratory; Eva L. Dyer; Liam Paninski; Cole L. Hurwitz (CoRR, 2024)

Generalizable autoregressive modeling of time series through functional narratives. - 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 (CoRR, 2024)

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 (J. Mach. Learn. Res., 2024)

Multi-session, multi-task neural decoding from distinct cell-types and brain regions. - Mehdi Azabou; Krystal Xuejing Pan; Vinam Arora; Ian Jarratt Knight; Eva L. Dyer; Blake Aaron Richards (ICLR, 2025)

In vivo cell-type and brain region classification via multimodal contrastive learning. - Han Yu; Hanrui Lyu; YiXun Xu; Charlie Windolf; Eric Kenji Lee; Fan Yang; Andrew M. Shelton; Olivier Winter; International Brain Laboratory; Eva L. Dyer; Chandramouli Chandrasekaran; Nicholas A. Steinmetz; et al. (ICLR, 2025)

Time Series Domain Adaptation via Channel-Selective Representation Alignment. - Nauman Ahad; Mark A. Davenport; Eva L. Dyer (Trans. Mach. Learn. Res., 2025)

Neural Encoding and Decoding at Scale. - Yizi Zhang; Yanchen Wang; Mehdi Azabou; Alexandre Andre; Zixuan Wang; Hanrui Lyu; International Brain Laboratory; Eva L. Dyer; Liam Paninski; Cole L. Hurwitz (CoRR, 2025)

Generalizable, real-time neural decoding with hybrid state-space models. - Avery Hee-Woon Ryoo; Nanda H. Krishna; Ximeng Mao; Mehdi Azabou; Eva L. Dyer; Matthew G. Perich; Guillaume Lajoie (CoRR, 2025)