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NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport

Mar 09, 2019
Matthew Hoffman, Pavel Sountsov, Joshua V. Dillon, Ian Langmore, Dustin Tran, Srinivas Vasudevan


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Simple, Distributed, and Accelerated Probabilistic Programming

Nov 29, 2018
Dustin Tran, Matthew Hoffman, Dave Moore, Christopher Suter, Srinivas Vasudevan, Alexey Radul, Matthew Johnson, Rif A. Saurous

* Appears in Neural Information Processing Systems, 2018. Code available at http://bit.ly/2JpFipt 

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Aerial Spectral Super-Resolution using Conditional Adversarial Networks

Dec 23, 2017
Aneesh Rangnekar, Nilay Mokashi, Emmett Ientilucci, Christopher Kanan, Matthew Hoffman


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Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models

Dec 21, 2017
Jesse Engel, Matthew Hoffman, Adam Roberts


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On the challenges of learning with inference networks on sparse, high-dimensional data

Oct 17, 2017
Rahul G. Krishnan, Dawen Liang, Matthew Hoffman

* 14 pages, 3 tables, 11 figures 

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The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM

Feb 20, 2016
Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams


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Learning Activation Functions to Improve Deep Neural Networks

Apr 21, 2015
Forest Agostinelli, Matthew Hoffman, Peter Sadowski, Pierre Baldi

* Accepted as a workshop paper contribution at the International Conference on Learning Representations (ICLR) 2015 

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