| Voltage imaging and optogenetics reveal behaviour-dependent changes in hippocampal dynamics Y Adam, JJ Kim, S Lou, Y Zhao, ME Xie, D Brinks, H Wu, ... Nature 569 (7756), 413-417, 2019 | 353 | 2019 |
| Plex: Towards reliability using pretrained large model extensions D Tran, J Liu, MW Dusenberry, D Phan, M Collier, J Ren, K Han, Z Wang, ... arXiv preprint arXiv:2207.07411, 2022 | 134 | 2022 |
| Deep ensembles work, but are they necessary? T Abe, EK Buchanan, G Pleiss, R Zemel, JP Cunningham Advances in Neural Information Processing Systems 35, 33646-33660, 2022 | 63 | 2022 |
| Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking A Wu, EK Buchanan, M Whiteway, M Schartner, G Meijer, JP Noel, ... Advances in Neural Information Processing Systems 33, 6040--6052, 2020 | 58* | 2020 |
| Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data EK Buchanan, I Kinsella, D Zhou, R Zhu, P Zhou, F Gerhard, J Ferrante, ... BioRxiv, 334706, 2018 | 50* | 2018 |
| Neuroscience Cloud Analysis As a Service: An open-source platform for scalable, reproducible data analysis T Abe, I Kinsella, S Saxena, EK Buchanan, J Couto, J Briggs, SL Kitt, ... Neuron 110 (17), 2771-2789. e7, 2022 | 39 | 2022 |
| Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders MR Whiteway, D Biderman, Y Friedman, M Dipoppa, EK Buchanan, A Wu, ... PLoS computational biology 17 (9), e1009439, 2021 | 35* | 2021 |
| Reproducibility of in vivo electrophysiological measurements in mice International Brain Laboratory, K Banga, J Benson, J Bhagat, D Biderman, ... bioRxiv, 2022.05. 09.491042, 2022 | 22 | 2022 |
| Pathologies of predictive diversity in deep ensembles T Abe, EK Buchanan, G Pleiss, JP Cunningham arXiv preprint arXiv:2302.00704, 2023 | 15 | 2023 |
| The Best Deep Ensembles Sacrifice Predictive Diversity T Abe, EK Buchanan, G Pleiss, JP Cunningham I Can't Believe It's Not Better Workshop: Understanding Deep Learning …, 2022 | 12 | 2022 |
| Quantifying the behavioral dynamics of C. elegans with autoregressive hidden Markov models EK Buchanan, A Lipschitz, SW Linderman, L Paninski Workshop on Worm’s Neural Information Processing at the 31st Conference on …, 2017 | 11 | 2017 |
| Archon: An architecture search framework for inference-time techniques J Saad-Falcon, AG Lafuente, S Natarajan, N Maru, H Todorov, E Guha, ... arXiv preprint arXiv:2409.15254, 2024 | 7 | 2024 |
| Semi-supervised sequence modeling for improved behavioral segmentation MR Whiteway, ES Schaffer, A Wu, EK Buchanan, OF Onder, N Mishra, ... bioRxiv, 2021.06. 16.448685, 2021 | 5 | 2021 |
| Video hardware and software for the International Brain Laboratory D Birman, N Bonacchi, K Buchanan, G Chapuis, JM Huntenburg, G Meijer, ... | 4 | 2022 |
| Constrained matrix factorization methods for denoising and demixing voltage imaging data EK Buchanan, J Friedrich, I Kinsella, P Stinson, P Zhou, F Gerhard, ... COSYNE, 2018 | 3 | 2018 |
| Reliability benchmarks for semantic segmentation EK Buchanan, MW Dusenberry, J Ren, KP Murphy, B Lakshminarayanan, ... NeurIPS 2022 Workshop on Distribution Shifts: Connecting Methods and …, 2022 | 2* | 2022 |
| On the interplay between learning and memory in deep state space models J Smekal, N Zucchet, D Biderman, EK Buchanan, JTH Smith, ... | 1 | 2024 |
| The Effects of Ensembling on Long-Tailed Data EK Buchanan, G Pleiss, Y Wang, JP Cunningham NeurIPS 2023 Workshop Heavy Tails in Machine Learning, 2023 | 1 | 2023 |
| Brain-to-Text Benchmark'24: Lessons Learned FR Willett, J Li, T Le, C Fan, M Chen, E Shlizerman, Y Chen, X Zheng, ... arXiv preprint arXiv:2412.17227, 2024 | | 2024 |
| Building Reliable Machine Learning Systems for Neuroscience EK Buchanan Columbia University, 2024 | | 2024 |
Liam PaninskiColumbia UniversityVerified email at stat.columbia.edu
