Papers

[Lab members are in bold, + denotes equal contribution ]

Preprints
  • 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, July 2021 (Paper, Code)
  • S. Thakoor, C. Tallec, M. Gheshlaghi Azar, M. Azabou, E.L. Dyer, R. Munos, P. Velickovic, M. Valko, Bootstrapped Representation Learning on Graphs, May 2021
  • 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, Feb 2021 (Preprint, Code)
  • M. Dabagia, K. Kording, E.L. Dyer, Comparing high-dimensional neural recordings by aligning their low-dimensional latent representations, Nature Biomedical Engineering, to appear 2021 (Preprint)

2021

  • 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, to appear at the IEEE International Conf. on Image Processing (ICIP), 2021 (Paper)
  • C-H. Lin, M. Azabou, E.L. Dyer, Making transport more robust and interpretable by moving data through a small number of anchor points, to appear at the International Conference on Machine Learning (ICML), 2021 (Paper, ICML Poster, Code)
  • C-H. Lin, JD Miano, EL Dyer, Bayesian optimization for modular black-box systems with switching costs, to appear at the Conference on Uncertainty in Artificial Intelligence (UAI), July 2021 (Preprint)

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 (Paper)
  • 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 (Paper, Preprint)
  • K. Shadi, E.L. Dyer, C. Dovrolis, Multi-sensory integration in the mouse cortical connectome using a network diffusion model, Network Neuroscience,  October 2020 (Paper, Preprint)
  • 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 (Preprint, X-ray Atlas)
  • R. Farhoodi, E.L. Dyer, J. Yarkony, S. Wang, K.P. Kording, Reconstructing the morphology of neurons from sparse noisy observations, under review at Plos Computational Biology, September 2020
  • A. Balwani, E.L. Dyer, A deep feature learning approach for mapping the brain’s microarchitecture and organization, ICML Workshop on ML Interpretability for Scientific Discovery, July 2020 (Preprint, ICML Workshop Paper)

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 (Paper)
  • K. Milligan, A. Balwani, E.L. Dyer, Brain Mapping at High Resolutions: Challenges and Opportunities, Current Opinion in Biomedical Engineering, 2019 (Paper)
  • 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) (Paper, Python Code, Matlab Code)
  • D. Rolnick, E.L. Dyer, Generative models and abstractions for large-scale neuroanatomy datasets, Current Opinion in Neurobiology, February 2019. (Paper)
2018
  • T.J. LaGrow, M. Moore, J.A. Prasad, A. Webber , M.A. Davenport, E.L. Dyer, Sparse Recovery Methods for Estimating Cytoarchitectonic Divisions, biorXiv, p.445742, 2018 (Preprint)
  • 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. (Paper)
  • 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, . (Paper)
  • 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 Society Annual Conference (EMBC), July 2018. (Paper)
  • 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. (Paper)
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. (Paper, 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, . (Paper, 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. (Paper, Code)

Selected Papers < 2017

  • M. Azar, E.L. Dyer, and K.P. Körding, Convex relaxation regression: Black-Box optimization of smooth functions by learning their convex envelopes, Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), June 2016. (Paper, Poster, Slides)
  • R.J. Patel, T.A. Goldstein, E.L. Dyer, A. Mirhoseini, and R.G. Baraniuk, Deterministic column sampling for low rank approximation: Nyström vs. Incomplete Cholesky Decomposition, SIAM Data Mining (SDM) Conference, May 2016. (Paper, Code)
  • E.L. Dyer, T.A. Goldstein, R.J. Patel, K.P. Körding, and R.G. Baraniuk: Sparse self-expressive decompositions for matrix approximation and clustering, arXiv:1505.00824 [cs.IT], 2015. (PaperCode)
  • E.L. Dyer, A.C. Sankaranarayanan, and R.G. Baraniuk, Greedy feature selection for subspace clustering, The Journal of Machine Learning Research 14 (1), 2487-2517, September 2013. (Paper)
  • 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. (Paper, Code)
Project Pages
Mine Your Own vieW (MYOW)
Latent OT (LOT)
DeepBrainDisco
PyHiWA
X-ray MicroCT Dataset