Preprints
- D. Lachi, M. Azabou, V. Arora, E.L. Dyer: GraphFM: A Scalable Framework for Multi-Graph Pretraining, arXiv, July 2024
- G. Tolossa, A. Schneider, E.L. Dyer, K.B. Hengen: A conserved code for anatomy: Neurons throughout the brain embed robust signatures of their anatomical location into spike trains, bioRxiv, July 2024
2024
- Z. Chen, C-H Lin, R. Liu, J. Xiao, E.L. Dyer: Your contrastive learning problem is secretly a distribution alignment problem, Advances in Neural Information Processing Systems, Dec 2024 [NeurIPS24]
- Y. Zhang, Y. Wang, D. Jimenez-Beneto, Z. Wang, M. Azabou, B. Richards, O. Winter, The International Brain Laboratory, E.L. Dyer, L. Paninski, C. Hurwitz: Towards a “universal translator” for neural dynamics at single-cell, single-spike resolution, Advances in Neural Information Processing Systems, Dec 2024 [NeurIPS24]
- C. Kaushik+, R. Liu+, C-H Lin, A. Khera, M.Y. Jin, W. Ma, V. Muthukumar, E.L. Dyer: Balanced data, imbalanced spectra: Unveiling class disparities with spectral imbalance, International Conference on Machine Learning, 2024 (+co-first authors) [ICML24]
- J.N. McGregor, C.A. Farris, S. Ensley, A. Schneider, C. Wang, Y. Liu, J. Tu, H. Elmore, K.D. Ronayne, R. Wessel, E.L. Dyer, K. Bhaskaran-Nair, D.M. Holtzman, K.B. Hengen: Tauopathy severely disrupts homeostatic set-points in emergent neural dynamics but not the activity of individual neurons, Neuron, Sept 2023 [Neuron]
- D.F. Parks+, A.M. Schneider+, Y. Xu, S.J. Brunwasser, S. Funderburk, D. Thurber, T. Blanche, E.L. Dyer, D. Haussler, K.B. Hengen: A non-oscillatory, millisecond-scale embedding of brain state provides insight into behavior, Nature Neuroscience, July 2024 [Nature Neuroscience]
- Jingyun Xiao, Ran Liu, Eva L. Dyer: GAFormer: Enhancing time-series transformers through group-aware embeddings, International Conference on Learning Representations (ICLR), 2024 [ICLR24]
- C-H Lin, C. Kaushik, E.L. Dyer+ & V. Muthukumar+: The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective, Journal of Machine Learning Research (JMLR), (+ co-last authors) [JMLR]
- R. Liu, S. Khose, J. Xiao, L. Sathidevi, K. Ramnath, Z. Kira, E.L. Dyer: LatentDR: Improving model generalization through sample-aware latent degradation and restoration, WACV, 2024 [WACV24]
2023
- E.L. Dyer, K.P. Kording: Why the simplest explanation isn’t always the best, Proceedings of the National Academy of Sciences (PNAS), Dec 2023 [PNAS]
- M. Azabou, V. Arora, V. Ganesh, X. Mao, S. Nachimuthu, M. Mendelson, B. Richards, M. Perich, G. Lajoie, E.L. Dyer: A unified, scalable framework for neural population decoding, NeurIPS 2023 [NeurIPS23] (Page)
- M. Azabou, M. Mendelson, N. Ahad, M. Sorokin, S. Thakoor, C. Urzay, E.L. Dyer: Relax, it doesn’t matter how you get there: A new self-supervised approach for multi-timescale behavior analysis, Advances in Neural Information Processing Systsems 2023 (Spotlight, 3% acceptance) (Web) [NeurIPS23]
- A. Ten Eyck, Y-C Chen, L. Gifford, D. Torres-Rivera, E.L. Dyer, G.B. Melikyan: Label-free imaging of nuclear membrane for analysis of nuclear import of viral complexes, Journal of Virological Methods, Volume 322, December 2023
- 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 [ICML23]
- A. Schneider+, M. Azabou+, L. McDougall-Vigier, D. Parks, S. Ensley, K. Bhaskaran-Nair, T. Nowakowski, E.L. Dyer+ & K.B. Hengen+: Transcriptomic cell type structures in vivo neuronal activity across multiple timescales, Cell Reports, 2023 (Code) [Cell Reports]
- G.O. Bozdag, S.A. Zamani-Dahaj, P.C. Kahn, T.C. Day, K. Tong, A.H. Balwani, E.L. Dyer, P.J. Yunker+ & W.C. Ratcliff+: De novo evolution of macroscopic multicellularity, Nature, 2023 [Nature]
- M. Mendelson, M. Azabou, S. Jacob, N. Grissom, D.P. Darrow, B. Ebitz, A. Herman, E.L. Dyer: Learning signatures of decision making from many individuals playing the same game, 11th IEEE EMBS Conference on Neural Engineering (NER’23), April 2023 [NER23]
- C. Urzay+, N. Ahad+, Mehdi Azabou, Aidan Schneider, Geethika Atmakuri, K.B. Hengen, E.L. Dyer: Detecting change points in neural population activity with contrastive metric learning, 11th IEEE EMBS Conference on Neural Engineering (NER’23), April 2023 [NER23]
2022
- R. Liu, M. Azabou, M. Dabagia, J. Xiao, E.L. Dyer: Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers, Advances in Neural Information Processing Systems (NeurIPS), Dec 2022 [NeurIPS22]
- J. Quesada+, L. Sathidevi+, R. Liu, N. Ahad, J.M. Jackson, M. Azabou, J. Xiao, C. Liding, C. Urzay, W. Gray-Roncal, E.C. Johnson+ & E.L. Dyer+: MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction, Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track, Dec 2022 (Page) [NeurIPS22]
- M. Azabou, M. Mendelson, M. Sorokin, S. Thakoor, N. Ahad, C. Urzay, E.L. Dyer: Learning Behavior Representations Through Multi-Timescale Bootstrapping, Conference on Vision and Pattern Recognition (CVPR) Workshop on Multi-Agent Behavior, 2022 [CVPR22]
- J.M. Jackson+, R. Liu+, E.L. Dyer: Building representations of different brain areas through hierarchical point cloud networks, Conference on Medical Imaging with Deep Learning (MIDL), 2022 [MIDL22]
- S. Thakoor, C. Tallec, M. Gheshlaghi Azar, M. Azabou, E.L. Dyer, R. Munos, P. Velickovic, M. Valko: Large-Scale Representation Learning on Graphs via Bootstrapping, International Conference on Learning Representations (ICLR), 2022 (Code) [ICLR22]
- M. Dabagia, K. Kording, E.L. Dyer: Aligning latent representations of neural activity, Nature Biomedical Engineering, 2022 [NatureBME]
- N. Ahad, E.L. Dyer, K.B. Hengen, Y. Xie, M.A. Davenport: Learning Sinkhorn divergences for supervised change point detection, arXiv, Feb 2022
- : Circuit-specific selective vulnerability in the DMN persists in the face of widespread amyloid burden, bioRxiv, Nov 2022
2021
- R. Liu, M. Azabou, M. Dabagia, C-H. Lin, M. Gheshlaghi Azar, K.B. Hengen, M. Valko, E.L. Dyer: Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity, Neural Information Processing Systems (NeurIPS), Oral (1% submissions), 2021 [NeurIPS21] (Code)
- M. Azabou, M. Gheshlaghi Azar, R. Liu, C-H. Lin, E.C. Johnson, K. Bhaskharan-Nair, M. Dabagia, K.B. Hengen, W. Gray-Roncal, M. Valko, E.L. Dyer: Mine Your Own vieW: Self-supervised learning through across-sample prediction, Neural Information Processing Systems (NeurIPS), Workshop on Self-supervised Learning: Theory and Practice, Oral, Dec 2021 [NeurIPS21] (Code)
- M. Azabou+, M. Dabagia+, R. Liu+, C-H. Lin, K.B. Hengen, E.L. Dyer: Using self-supervision and augmentations to build insights into neural coding, Neural Information Processing Systems (NeurIPS), Workshop on Self-supervised Learning: Theory and Practice, Dec 2021 [NeurIPS21]
- F. Pei+, J. Ye+, D. Zoltowski, A. Wu, R.H. Chowdhury, H. Sohn, J.E. O’Doherty, K.V. Shenoy, M.T. Kaufman, M. Churchland, M. Jazayeri, L.E. Miller, J. Pillow, I.M. Park, E.L. Dyer, C. Pandarinath: Neural Latents Benchmark ’21: Evaluating latent variable models of neural population activity, Neural Information Processing Systems (NeurIPS), Benchmark and Datasets Track, 2021 [NeurIPS21] (Page)
- A. Balwani+, J. Miano+, R. Liu, L. Kitchell, J.A. Prasad, E.C. Johnson, W. Gray-Roncal, E.L. Dyer: Multi-scale modeling of neural structure in X-ray imagery, IEEE International Conference on Image Processing, 2021 [ICIP21]
- C-H. Lin, M. Azabou, E.L. Dyer: Making transport more robust and interpretable by moving data through a small number of anchor points, International Conference on Machine Learning, 2021 (Poster, Code) [ICML21]
- C-H. Lin, J.D. Miano, E.L. Dyer: Bayesian optimization for modular black-box systems with switching costs, Conference on Uncertainty in Artificial Intelligence (UAI), July 2021 [UAI21]
2020
- E.C. Johnson, M. Wilt, L.M. Rodriguez, R. Tenazas, C. Rivera, N. Drenkow, D. Kleissas, T.J. LaGrow, H. Cowley, J. Downs, J. Matelsky, M. Hughes, E. Reilly, B. Wester, E.L. Dyer, K.P. Kording, W. Gray-Roncal: Toward a Reproducible, Scalable, Framework for Processing Large Neuroimaging Datasets, GigaScience, Volume 9, Issue 12, December 2020
- R. Liu, C. Subakan, A. Balwani, J. Whitesell, J.A. Harris, S. Koyejo, E.L. Dyer: A generative modeling approach for interpreting population-level variability in brain structure, International Conference on Medical Image Computing & Computer Assisted Intervention (MICCAI), October 2020
- M. Jas, T. Achakulvisut, A. Idrizović, D. Acuna, M. Antalek, V. Marques, T. Odland, R.P. Garg, M. Agrawal, Y. Umegaki, P. Foley, H. Fernandes, D. Harris, B. Li, O. Pieters, S. Otterson, G. De Toni, C. Rodgers, E.L. Dyer, M Hamalainen, K.P. Kording, P. Ramkumar: Pyglmnet: Python implementation of elastic-net regularized generalized linear models, J Open Source Software, 5:47, 2020 (Code)
- K. Shadi, E.L. Dyer, C. Dovrolis: Multi-sensory integration in the mouse cortical connectome using a network diffusion model, Network Neuroscience, October 2020
- J.A. Prasad, A.H. Balwani, E.C. Johnson, J.D. Miano, V. Sampathkumar, V. de Andrade, K. Fezzaa, M. Du, R. Vescovi, C. Jacobsen, K.P. Kording, D. Gursoy, W. Gray Roncal, N. Kasthuri, E.L. Dyer: A three-dimensional thalamocortical dataset for characterizing brain heterogeneity, Nature Scientific Data, October 2020 (Interactive Atlas)
- [Nature Scientific Data]A. Balwani, E.L. Dyer: A deep feature learning approach for mapping the brain’s microarchitecture and organization, International Conference on Machine Learning (ICML), Workshop on ML Interpretability for Scientific Discovery, July 2020 (Extended version)
2019
- A. Balwani, E.L. Dyer, Modeling variability in brain architecture with deep feature learning, IEEE Asilomar Conference on Signals, Systems, and Computers, Dec 2019
- K. Milligan, A. Balwani, E.L. Dyer, Brain Mapping at High Resolutions: Challenges and Opportunities, Current Opinion in Biomedical Engineering, 2019
- J. Lee, M. Dabagia, E.L. Dyer+, C. Rozell+, Hierarchical Optimal Transport for Multimodal Distribution Alignment, Neural Information Processing Systems (NeurIPS), Dec 2019. (+ equal contribution) (Code)
- D. Rolnick, E.L. Dyer, Generative models and abstractions for large-scale neuroanatomy datasets, Current Opinion in Neurobiology, Feb 2019
2018
- C. Pandarinath, K.C. Ames, A.A. Russo, A. Farshchian, L.E. Miller, E.L. Dyer, J.C. Kao, Latent factors and dynamics in motor cortex and their application to brain-machine interfaces, .
- T. J. Lee, A. Kumar, A. Balwani, D. Brittain, S. Kinn, C. A. Tovey, E. L. Dyer, N.M. da Costa, R.C. Reid, C.R. Forest, D.J. Bumbarger, Large-scale neuroanatomy using LASSO: Loop-based Automated Serial Sectioning Operation, Plos ONE, October 2018
- T.J. LaGrow, M. Moore, J.A. Prasad, M.A. Davenport, E.L. Dyer, Approximating Cellular Densities from High-Resolution Neuroanatomical Imaging Data, IEEE Engineering in Medicine and Biology Annual Conference (EMBC), July 2018
- X. Yang, V. De Andrade, W. Scullin, E. L. Dyer, F. De Carlo, N. Kasthuri, Doga Gürsoy, Enhancing low-dose X-ray tomography through a deep convolutional neural network, Nature Scientific Reports 8, 2018
2017
- E.L. Dyer, M. Azar, H.L. Fernandes, M. Perich, L.E. Miller, and K.P. Körding, A cryptography-based approach to movement decoding, Nature Biomedical Engineering, 1(12), 967. 2017. (Code)
- E.L. Dyer, W.G. Roncal, J.A. Prasad, H.L. Fernandes, D. Gürsoy, V. De Andrade, K. Fezzaa, X. Xiao, J.T. Vogelstein, C. Jacobsen, K.P. Körding & N. Kasthuri, Quantifying mesoscale neuroanatomy using X-ray microtomography, . (Code, Data)
- A. Mirhoseini, E.L. Dyer, E. Songhori, R.G. Baraniuk, and F. Koushanfar, RankMap: A platform-aware framework for distributed learning from dense datasets, IEEE Transactions on Neural Networks and Learning Systems, 2017. (Code)
Pre-GT
- M. Azar, E.L. Dyer, and K.P. Körding, Convex relaxation regression: Black-Box optimization of smooth functions by learning their convex envelopes, Conference on Uncertainty in Artificial Intelligence (UAI), June 2016. (Poster, Slides)
- E.L. Dyer, A.C. Sankaranarayanan, and R.G. Baraniuk, Greedy feature selection for subspace clustering, The Journal of Machine Learning Research (JMLR) 14 (1), 2487-2517, September 2013
- E.L. Dyer, C. Studer, J.T. Robinson, and R.G Baraniuk, A robust and efficient method to recover neural events from noisy and corrupted data, IEEE EMBS Neural Engineering (NER) Conference, 2013. (Code)
