Eva L. Dyer - RankMe

74 papers A* 17A 5B 1Misc 2Journal 42Unranked 7

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks. CoRR Georgios Mentzelopoulos, Ioannis Asmanis, Konrad P. Kording, Eva L. Dyer, Kostas Daniilidis, Flavia Vitale
2025	J	`jnl`	Generalizable, real-time neural decoding with hybrid state-space models. CoRR Avery Hee-Woon Ryoo, Nanda H. Krishna, Ximeng Mao, Mehdi Azabou, Eva L. Dyer, Matthew G. Perich, Guillaume Lajoie
2025	J	`jnl`	GraphFM: A generalist graph transformer that learns transferable representations across diverse domains. Trans. Mach. Learn. Res. Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L. Dyer
2025	A*	`conf`	In vivo cell-type and brain region classification via multimodal contrastive learning. ICLR 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
2025	J	`jnl`	Integrating Temporal and Structural Context in Graph Transformers for Relational Deep Learning. CoRR Divyansha Lachi, Mahmoud Mohammadi, Joe Meyer, Vinam Arora, Tom Palczewski, Eva L. Dyer
2025	J	`jnl`	Know Thyself by Knowing Others: Learning Neuron Identity from Population Context. CoRR Vinam Arora, Divyansha Lachi, Ian Jarratt Knight, Mehdi Azabou, Blake A. Richards, Cole L. Hurwitz, Joshua H. Siegle, Eva L. Dyer
2025	J	`jnl`	Learning Sinkhorn Divergences for Change Point Detection. IEEE Trans. Signal Process. Nauman Ahad, Eva L. Dyer, Keith B. Hengen, Yao Xie, Mark A. Davenport
2025	A*	`conf`	Multi-session, multi-task neural decoding from distinct cell-types and brain regions. ICLR Mehdi Azabou, Krystal Xuejing Pan, Vinam Arora, Ian Jarratt Knight, Eva L. Dyer, Blake Aaron Richards
2025	A*	`conf`	Neural Encoding and Decoding at Scale. ICML Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole Lincoln Hurwitz
2025	J	`jnl`	Neural Encoding and Decoding at Scale. CoRR Yizi Zhang, Yanchen Wang, Mehdi Azabou, Alexandre Andre, Zixuan Wang, Hanrui Lyu, International Brain Laboratory, Eva L. Dyer, Liam Paninski, Cole L. Hurwitz
2025	J	`jnl`	PRISM: A hierarchical multiscale approach for time series forecasting. CoRR Zihao Chen, Alexandre Andre, Wenrui Ma, Ian Knight, Sergey Shuvaev, Eva L. Dyer
2025	J	`jnl`	RELATE: A Schema-Agnostic Perceiver Encoder for Multimodal Relational Graphs. CoRR Joe Meyer, Divyansha Lachi, Mahmoud Mohammadi, Roshan Reddy Upendra, Eva L. Dyer, Mark Li, Tom Palczewski
2025	J	`jnl`	Time Series Domain Adaptation via Channel-Selective Representation Alignment. Trans. Mach. Learn. Res. Nauman Ahad, Mark A. Davenport, Eva L. Dyer
2025	J	`jnl`	Your contrastive learning problem is secretly a distribution alignment problem. CoRR Zihao Chen, Chi-Heng Lin, Ran Liu, Jingyun Xiao, Eva L. Dyer
2024	A*	`conf`	Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. ICML Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer
2024	J	`jnl`	Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance. CoRR Chiraag Kaushik, Ran Liu, Chi-Heng Lin, Amrit Khera, Matthew Y. Jin, Wenrui Ma, Vidya Muthukumar, Eva L. Dyer
2024	A*	`conf`	GAFormer: Enhancing Timeseries Transformers Through Group-Aware Embeddings. ICLR Jingyun Xiao, Ran Liu, Eva L. Dyer
2024	J	`jnl`	Generalizable autoregressive modeling of time series through functional narratives. CoRR 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
2024	J	`jnl`	GraphFM: A Scalable Framework for Multi-Graph Pretraining. CoRR Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva L. Dyer
2024	A	`conf`	LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration. WACV Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L. Dyer
2024	J	`jnl`	The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. J. Mach. Learn. Res. Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar
2024	A*	`conf`	Towards a "Universal Translator" for Neural Dynamics at Single-Cell, Single-Spike Resolution. NeurIPS 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
2024	J	`jnl`	Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution. CoRR 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
2024	A*	`conf`	Your contrastive learning problem is secretly a distribution alignment problem. NeurIPS Zihao Chen, Chi-Heng Lin, Ran Liu, Jingyun Xiao, Eva L. Dyer
2023	A*	`conf`	A Unified, Scalable Framework for Neural Population Decoding. NeurIPS Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer
2023	J	`jnl`	A Unified, Scalable Framework for Neural Population Decoding. CoRR Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael J. Mendelson, Blake A. Richards, Matthew G. Perich, Guillaume Lajoie, Eva L. Dyer
2023	—	`conf`	Detecting change points in neural population activity with contrastive metric learning. NER Carolina Urzay, Nauman Ahad, Mehdi Azabou, Aidan Schneider, Geethika Atamkuri, Keith B. Hengen, Eva L. Dyer
2023	A*	`conf`	Half-Hop: A graph upsampling approach for slowing down message passing. ICML Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer
2023	J	`jnl`	Half-Hop: A graph upsampling approach for slowing down message passing. CoRR Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Velickovic, Eva L. Dyer
2023	J	`jnl`	LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration. CoRR Ran Liu, Sahil Khose, Jingyun Xiao, Lakshmi Sathidevi, Keerthan Ramnath, Zsolt Kira, Eva L. Dyer
2023	—	`conf`	Learning signatures of decision making from many individuals playing the same game. NER Michael J. Mendelson, Mehdi Azabou, Suma Jacob, Nicola Grissom, David Darrow, Becket Ebitz, Alexander Herman, Eva L. Dyer
2023	J	`jnl`	Learning signatures of decision making from many individuals playing the same game. CoRR Michael J. Mendelson, Mehdi Azabou, Suma Jacob, Nicola Grissom, David Darrow, Becket Ebitz, Alexander Herman, Eva L. Dyer
2023	J	`jnl`	MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. CoRR 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
2023	A*	`conf`	Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis. NeurIPS Mehdi Azabou, Michael Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay, Eva L. Dyer
2023	J	`jnl`	Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis. CoRR Mehdi Azabou, Michael Mendelson, Nauman Ahad, Maks Sorokin, Shantanu Thakoor, Carolina Urzay, Eva L. Dyer
2022	A*	`conf`	Large-Scale Representation Learning on Graphs via Bootstrapping. ICLR Shantanu Thakoor, Corentin Tallec, Mohammad Gheshlaghi Azar, Mehdi Azabou, Eva L. Dyer, Rémi Munos, Petar Velickovic, Michal Valko
2022	J	`jnl`	Learning Behavior Representations Through Multi-Timescale Bootstrapping. CoRR Mehdi Azabou, Michael Mendelson, Maks Sorokin, Shantanu Thakoor, Nauman Ahad, Carolina Urzay, Eva L. Dyer
2022	J	`jnl`	Learning Sinkhorn divergences for supervised change point detection. CoRR Nauman Ahad, Eva L. Dyer, Keith B. Hengen, Yao Xie, Mark A. Davenport
2022	A*	`conf`	MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction. NeurIPS 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
2022	A*	`conf`	Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. NeurIPS Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer
2022	J	`jnl`	Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers. CoRR Ran Liu, Mehdi Azabou, Max Dabagia, Jingyun Xiao, Eva L. Dyer
2022	J	`jnl`	The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective. CoRR Chi-Heng Lin, Chiraag Kaushik, Eva L. Dyer, Vidya Muthukumar
2021	A	`conf`	Bayesian optimization for modular black-box systems with switching costs. UAI Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer
2021	A*	`conf`	Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. NeurIPS Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer
2021	J	`jnl`	Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity. CoRR Ran Liu, Mehdi Azabou, Max Dabagia, Chi-Heng Lin, Mohammad Gheshlaghi Azar, Keith B. Hengen, Michal Valko, Eva L. Dyer
2021	A*	`conf`	Making transport more robust and interpretable by moving data through a small number of anchor points. ICML Chi-Heng Lin, Mehdi Azabou, Eva L. Dyer
2021	J	`jnl`	Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction. CoRR 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
2021	B	`conf`	Multi-Scale Modeling of Neural Structure in X-Ray Imagery. ICIP Aishwarya H. Balwani, Joseph D. Miano, Ran Liu, Lindsey Kitchell, Judy A. Prasad, Erik C. Johnson, William R. Gray Roncal, Eva L. Dyer
2021	—	`conf`	Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. NeurIPS Datasets and Benchmarks 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
2021	J	`jnl`	Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity. CoRR 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
2020	—	`conf`	A Generative Modeling Approach for Interpreting Population-Level Variability in Brain Structure. MICCAI (5) Ran Liu, Cem Subakan, Aishwarya H. Balwani, Jennifer D. Whitesell, Julie Harris, Sanmi Koyejo, Eva L. Dyer
2020	J	`jnl`	Bayesian optimization for modular black-box systems with switching costs. CoRR Chi-Heng Lin, Joseph D. Miano, Eva L. Dyer
2020	J	`jnl`	Making transport more robust and interpretable by moving data through a small number of anchor points. CoRR Chi-Heng Lin, Mehdi Azabou, Eva L. Dyer
2020	J	`jnl`	Pyglmnet: Python implementation of elastic-net regularized generalized linear models. J. Open Source Softw. 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
2019	A*	`conf`	Hierarchical Optimal Transport for Multimodal Distribution Alignment. NeurIPS John Lee, Max Dabagia, Eva L. Dyer, Christopher Rozell
2019	J	`jnl`	Hierarchical Optimal Transport for Multimodal Distribution Alignment. CoRR John Lee, Max Dabagia, Eva L. Dyer, Christopher J. Rozell
2019	Misc	`conf`	Modeling Variability in Brain Architecture with Deep Feature Learning. ACSSC Aishwarya H. Balwani, Eva L. Dyer
2018	—	`conf`	Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data. EMBC Theodore J. LaGrow, Michael G. Moore, Judy A. Prasad, Mark A. Davenport, Eva L. Dyer
2018	J	`jnl`	RankMap: A Framework for Distributed Learning From Dense Data Sets. IEEE Trans. Neural Networks Learn. Syst. Azalia Mirhoseini, Eva L. Dyer, Ebrahim M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar
2016	A	`conf`	Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. UAI Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding
2016	J	`jnl`	Convex Relaxation Regression: Black-Box Optimization of Smooth Functions by Learning Their Convex Envelopes. CoRR Mohammad Gheshlaghi Azar, Eva L. Dyer, Konrad P. Körding
2016	A	`conf`	Deterministic Column Sampling for Low-Rank Matrix Approximation: Nyström vs. Incomplete Cholesky Decomposition. SDM Raajen Patel, Tom Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk
2016	J	`jnl`	From sample to knowledge: Towards an integrated approach for neuroscience discovery. CoRR William R. Gray Roncal, Eva L. Dyer, Doga Gürsoy, Konrad P. Körding, Narayanan Kasthuri
2016	J	`jnl`	Quantifying mesoscale neuroanatomy using X-ray microtomography. CoRR Eva L. Dyer, William R. Gray Roncal, Hugo L. Fernandes, Doga Gürsoy, Xianghui Xiao, Joshua T. Vogelstein, Chris Jacobsen, Konrad P. Körding, Narayanan Kasthuri
2015	J	`jnl`	RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets. CoRR Azalia Mirhoseini, Eva L. Dyer, Ebrahim M. Songhori, Richard G. Baraniuk, Farinaz Koushanfar
2015	J	`jnl`	Self-Expressive Decompositions for Matrix Approximation and Clustering. CoRR Eva L. Dyer, Tom Goldstein, Raajen Patel, Konrad P. Körding, Richard G. Baraniuk
2015	J	`jnl`	oASIS: Adaptive Column Sampling for Kernel Matrix Approximation. CoRR Raajen Patel, Thomas A. Goldstein, Eva L. Dyer, Azalia Mirhoseini, Richard G. Baraniuk
2013	J	`jnl`	Greedy Feature Selection for Subspace Clustering CoRR Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk
2013	J	`jnl`	Greedy feature selection for subspace clustering. J. Mach. Learn. Res. Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk
2013	Misc	`conf`	Subspace clustering with dense representations. ICASSP Eva L. Dyer, Christoph Studer, Richard G. Baraniuk
2011	A*	`conf`	Hybrid modeling of non-stationary process variations. DAC Eva L. Dyer, Mehrdad Majzoobi, Farinaz Koushanfar
2010	A	`conf`	Rapid FPGA delay characterization using clock synthesis and sparse sampling. ITC Mehrdad Majzoobi, Eva L. Dyer, Ahmed Elnably, Farinaz Koushanfar
2010	—	`conf`	Recovering Spikes from Noisy Neuronal Calcium Signals via Structured Sparse Approximation. LVA/ICA Eva L. Dyer, Marco F. Duarte, Don H. Johnson, Richard G. Baraniuk
2007	—	`conf`	Validation System of MR Image Overlay and Other Needle Insertion Techniques. MMVR Gregory S. Fischer, Eva L. Dyer, Csaba Csoma, Anton Deguet, Gabor Fichtinger