Deep Ensembles Work, But Are They Necessary?

Ensembling neural networks is an effective way to increase accuracy, and improve uncertainty quantification and robustness to dataset shift. But are these gains unique? We show that they are not and that single larger models can give us similar benefits as ensembles of smaller models.

Taiga Abe*, E. Kelly Buchanan*, Geoff Pleiss, Rich Zemel, John Cunningham

arxiv 2022 | code | bibtex

Partitioning variability in animal behavioral videos using semi-supervised variational autoencoders

Some behavioral features, such as chewing or grimacing, cannot be easily tracked. In this work we disentangle such behaviors in video data by incorporating the other features which can be easily tracked.

Matthew R Whiteway, Dan Biderman, Yoni Friedman, Mario Dipoppa, E. Kelly Buchanan, Anqi Wu, John Zhou, Jean-Paul R Noel, John Cunningham, Liam Paninski

arxiv 2021 | code | bibtex

Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking

Can we exploit the spatial and temporal structure inherent in behavioral videos for pose tracking in the low data regime? Yes, thanks to pseudolabels and probabilistic graphical models.

Anqi Wu*, E. Kelly Buchanan*, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory, John Cunningham, Liam Paninski

NEURIPS 2020 | code | bibtex

Penalized matrix decomposition for denoising, compression, and improved demixing of funcational imaging data

This paper shows that we can efficiently separate activity from different neurons through a rank-1 penalized matrix decomposition.

E. Kelly Buchanan*, Ian Kinsella*, Ding Zhou*, Rong Zhu, Pengcheng Zhou, Felipe Gerhard, John Ferrante, Ying Ma, Sharon Kim, Mohammed Shaik, Yajie Liang, Rongwen Lu, Jacob Reimer, Paul Fahey, Taliah Muhammad, Graham Dempsey, Elizabeth Hillman, Na Ji, Andreas Tolias, Liam Paninski

arXiv 2019 | code | bibtex | press

Quantifying the behavioral dynamics of C. elegans with autoregressive hidden Markov models

In this paper we extract interpretable behavioral syllables of C. elegans using a class of switching linear dynamical systems (Spotlight presentation)

E. Kelly Buchanan, Akiva Lipshitz, Scott Linderman, Liam Paninski

NEURIPS 2017 WNIP | code | bibtex