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Semantic Segmentation with Active Semi-Supervised Learning



Aneesh Rangnekar , Christopher Kanan , Matthew Hoffman


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Launchpad: A Programming Model for Distributed Machine Learning Research



Fan Yang , Gabriel Barth-Maron , Piotr Stańczyk , Matthew Hoffman , Siqi Liu , Manuel Kroiss , Aedan Pope , Alban Rrustemi


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Regularized Behavior Value Estimation



Caglar Gulcehre , Sergio Gómez Colmenarejo , Ziyu Wang , Jakub Sygnowski , Thomas Paine , Konrad Zolna , Yutian Chen , Matthew Hoffman , Razvan Pascanu , Nando de Freitas


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



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


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Simple, Distributed, and Accelerated Probabilistic Programming



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



Aneesh Rangnekar , Nilay Mokashi , Emmett Ientilucci , Christopher Kanan , Matthew Hoffman


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Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models



Jesse Engel , Matthew Hoffman , Adam Roberts


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



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



Ardavan Saeedi , Matthew Hoffman , Matthew Johnson , Ryan Adams


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