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Attentive Clustering Processes


Oct 29, 2020
Ari Pakman, Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski


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A zero-inflated gamma model for deconvolved calcium imaging traces


Jun 05, 2020
Xue-Xin Wei, Ding Zhou, Andres Grosmark, Zaki Ajabi, Fraser Sparks, Pengcheng Zhou, Mark Brandon, Attila Losonczy, Liam Paninski

* Neurons, Behavior, Data Analysis, and Theory, 2020 
* Accepted for publication in Neurons, Behavior, Data analysis, and Theory 

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Disentangled sticky hierarchical Dirichlet process hidden Markov model


Apr 06, 2020
Ding Zhou, Yuanjun Gao, Liam Paninski


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General linear-time inference for Gaussian Processes on one dimension


Mar 11, 2020
Jackson Loper, David Blei, John P. Cunningham, Liam Paninski

* 36 pages, 5 figures, submitted to ICML 

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Discrete Neural Processes


Dec 28, 2018
Ari Pakman, Liam Paninski

* arXiv admin note: text overlap with arXiv:1811.09747 

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Amortized Bayesian inference for clustering models


Nov 24, 2018
Ari Pakman, Liam Paninski

* Presented at [email protected] 2018 Workshop 

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A Novel Variational Family for Hidden Nonlinear Markov Models


Nov 06, 2018
Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John Cunningham, Liam Paninski

* 6 figs, Submitted to ICLR 

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


Jul 17, 2018
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

* 36 pages, 18 figures 

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Reparameterizing the Birkhoff Polytope for Variational Permutation Inference


Oct 26, 2017
Scott W. Linderman, Gonzalo E. Mena, Hal Cooper, Liam Paninski, John P. Cunningham


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Stochastic Bouncy Particle Sampler


Jun 14, 2017
Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski

* ICML Camera ready version 

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Recurrent switching linear dynamical systems


Oct 26, 2016
Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson

* 15 pages, 6 figures 

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Linear dynamical neural population models through nonlinear embeddings


Oct 25, 2016
Yuanjun Gao, Evan Archer, Liam Paninski, John P. Cunningham

* NIPS 2016 

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Robust and scalable Bayesian analysis of spatial neural tuning function data


Jun 24, 2016
Kamiar Rahnama Rad, Timothy A. Machado, Liam Paninski


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Partition Functions from Rao-Blackwellized Tempered Sampling


May 25, 2016
David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

* 15 pages, 8 figures; Appearing at International Conference on Machine Learning 2016 

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Neuroprosthetic decoder training as imitation learning


Mar 14, 2016
Josh Merel, David Carlson, Liam Paninski, John P. Cunningham


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Black box variational inference for state space models


Nov 23, 2015
Evan Archer, Il Memming Park, Lars Buesing, John Cunningham, Liam Paninski


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