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Decomposing reverse-mode automatic differentiation


May 20, 2021
Roy Frostig, Matthew J. Johnson, Dougal Maclaurin, Adam Paszke, Alexey Radul

* Presented at the LAFI 2021 workshop at POPL, 17 January 2021 

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SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning


Feb 20, 2019
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine

* under review for ICML 2019 

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Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language


Nov 29, 2018
Matthew D. Hoffman, Matthew J. Johnson, Dustin Tran

* Appears in Neural Information Processing Systems, 2018. Code available at https://github.com/google-research/autoconj 

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The LORACs prior for VAEs: Letting the Trees Speak for the Data


Oct 16, 2018
Sharad Vikram, Matthew D. Hoffman, Matthew J. Johnson


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SOLAR: Deep Structured Latent Representations for Model-Based Reinforcement Learning


Aug 28, 2018
Marvin Zhang, Sharad Vikram, Laura Smith, Pieter Abbeel, Matthew J. Johnson, Sergey Levine


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Estimating the Spectral Density of Large Implicit Matrices


Feb 09, 2018
Ryan P. Adams, Jeffrey Pennington, Matthew J. Johnson, Jamie Smith, Yaniv Ovadia, Brian Patton, James Saunderson


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Composing graphical models with neural networks for structured representations and fast inference


Jul 07, 2017
Matthew J. Johnson, David Duvenaud, Alexander B. Wiltschko, Sandeep R. Datta, Ryan P. Adams

* v5 fixes tex compilation bugs and also a math bug in the statement and proof of Prop. 4.1 (and D.3). v4 adds two paragraphs to the related work section and fixes typos in the appendices. v3 fixes some typos in the appendices. v2 is a rewrite from v1 to be more readable and to include detailed appendices 

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Multimodal Prediction and Personalization of Photo Edits with Deep Generative Models


Apr 17, 2017
Ardavan Saeedi, Matthew D. Hoffman, Stephen J. DiVerdi, Asma Ghandeharioun, Matthew J. Johnson, Ryan P. Adams


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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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Dependent Multinomial Models Made Easy: Stick Breaking with the P贸lya-Gamma Augmentation


Jun 18, 2015
Scott W. Linderman, Matthew J. Johnson, Ryan P. Adams


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Detailed Derivations of Small-Variance Asymptotics for some Hierarchical Bayesian Nonparametric Models


Dec 31, 2014
Jonathan H. Huggins, Ardavan Saeedi, Matthew J. Johnson

* 7 pages 

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A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation


Nov 27, 2014
Scott W. Linderman, Matthew J. Johnson, Matthew A. Wilson, Zhe Chen


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Bayesian Nonparametric Hidden Semi-Markov Models


Sep 07, 2012
Matthew J. Johnson, Alan S. Willsky


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The Hierarchical Dirichlet Process Hidden Semi-Markov Model


Mar 15, 2012
Matthew J. Johnson, Alan Willsky

* Appears in Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence (UAI2010) 

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