Dyer, Eva L

18 publications

NeurIPS 2025 A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks Georgios Mentzelopoulos, Ioannis Asmanis, Konrad Kording, Eva L Dyer, Kostas Daniilidis, Flavia Vitale

NeurIPS 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

TMLR 2025 GraphFM: A Generalist Graph Transformer That Learns Transferable Representations Across Diverse Domains Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L Dyer

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, Liam Paninski, Cole Lincoln Hurwitz

NeurIPS 2025 Know Thyself by Knowing Others: Learning Neuron Identity from Population Context Vinam Arora, Divyansha Lachi, Ian Jarratt Knight, Mehdi Azabou, Blake Aaron Richards, Cole Lincoln Hurwitz, Josh Siegle, Eva L Dyer

ICLR 2025 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

ICML 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 Lincoln Hurwitz

TMLR 2025 Time Series Domain Adaptation via Channel-Selective Representation Alignment Nauman Ahad, Mark A. Davenport, Eva L Dyer

ICML 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

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

WACV 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

JMLR 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

NeurIPS 2024 Your Contrastive Learning Problem Is Secretly a Distribution Alignment Problem Zihao Chen, Chi-Heng Lin, Ran Liu, Jingyun Xiao, Eva L. Dyer

ICML 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 Veličković, Eva L Dyer

ICLR 2022 Large-Scale Representation Learning on Graphs via Bootstrapping Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L Dyer, Remi Munos, Petar Veličković, Michal Valko

UAI 2021 Bayesian Optimization for Modular Black-Box Systems with Switching Costs Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer

UAI 2016 Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding

JMLR 2013 Greedy Feature Selection for Subspace Clustering Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk