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Vitruvion: A Generative Model of Parametric CAD Sketches


Sep 29, 2021
Ari Seff, Wenda Zhou, Nick Richardson, Ryan P. Adams


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Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability


Jul 13, 2021
Dibya Ghosh, Jad Rahme, Aviral Kumar, Amy Zhang, Ryan P. Adams, Sergey Levine

* First two authors contributed equally 

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Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate


Jun 16, 2021
Xingyuan Sun, Tianju Xue, Szymon M. Rusinkiewicz, Ryan P. Adams

* 16 pages, 9 figures 

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Active multi-fidelity Bayesian online changepoint detection


Mar 26, 2021
Gregory W. Gundersen, Diana Cai, Chuteng Zhou, Barbara E. Engelhardt, Ryan P. Adams


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Randomized Automatic Differentiation


Jul 20, 2020
Deniz Oktay, Nick McGreivy, Joshua Aduol, Alex Beatson, Ryan P. Adams


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SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design


Jul 16, 2020
Ari Seff, Yaniv Ovadia, Wenda Zhou, Ryan P. Adams


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Learning Composable Energy Surrogates for PDE Order Reduction


May 15, 2020
Alex Beatson, Jordan T. Ash, Geoffrey Roeder, Tianju Xue, Ryan P. Adams


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SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models


Apr 01, 2020
Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen

* ICLR 2020 

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On the Difficulty of Warm-Starting Neural Network Training


Oct 18, 2019
Jordan T. Ash, Ryan P. Adams


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Discrete Object Generation with Reversible Inductive Construction


Jul 18, 2019
Ari Seff, Wenda Zhou, Farhan Damani, Abigail Doyle, Ryan P. Adams


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A Theoretical Connection Between Statistical Physics and Reinforcement Learning


Jun 24, 2019
Jad Rahme, Ryan P. Adams


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SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers


May 28, 2019
Igor Fedorov, Ryan P. Adams, Matthew Mattina, Paul N. Whatmough


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Efficient Optimization of Loops and Limits with Randomized Telescoping Sums


May 16, 2019
Alex Beatson, Ryan P. Adams


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Predicting Electron-Ionization Mass Spectrometry using Neural Networks


Nov 21, 2018
Jennifer N. Wei, David Belanger, Ryan P. Adams, D. Sculley

* 12 pages, 5 figures, accepted to Machine Learning for Molecules and Materials Workshop at NeurIPS 2018 

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Approximate Inference for Constructing Astronomical Catalogs from Images


Oct 12, 2018
Jeffrey Regier, Andrew C. Miller, David Schlegel, Ryan P. Adams, Jon D. McAuliffe, Prabhat

* major revision for AoAS 

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Motivating the Rules of the Game for Adversarial Example Research


Jul 20, 2018
Justin Gilmer, Ryan P. Adams, Ian Goodfellow, David Andersen, George E. Dahl


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Compressibility and Generalization in Large-Scale Deep Learning


May 21, 2018
Wenda Zhou, Victor Veitch, Morgane Austern, Ryan P. Adams, Peter Orbanz

* 14 pages, 1 figure. Minor phrasing changes and better notation for proofs 

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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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Automatic chemical design using a data-driven continuous representation of molecules


Dec 05, 2017
Rafael Gómez-Bombarelli, Jennifer N. Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D. Hirzel, Ryan P. Adams, Alán Aspuru-Guzik

* 26 pages, 8 figures 

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PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference


Nov 13, 2017
Jonathan H. Huggins, Ryan P. Adams, Tamara Broderick

* In Proceedings of the 31st Annual Conference on Neural Information Processing Systems (NIPS 2017) 

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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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Reducing Reparameterization Gradient Variance


May 22, 2017
Andrew C. Miller, Nicholas J. Foti, Alexander D'Amour, Ryan P. Adams


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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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Variational Boosting: Iteratively Refining Posterior Approximations


Feb 19, 2017
Andrew C. Miller, Nicholas Foti, Ryan P. Adams

* 25 pages, 9 figures, 2 tables 

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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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Bayesian latent structure discovery from multi-neuron recordings


Oct 26, 2016
Scott W. Linderman, Ryan P. Adams, Jonathan W. Pillow

* 11 pages, 5 figures, to appear in Advances in Neural Information Processing Systems 2016 

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A General Framework for Constrained Bayesian Optimization using Information-based Search


Sep 04, 2016
José Miguel Hernández-Lobato, Michael A. Gelbart, Ryan P. Adams, Matthew W. Hoffman, Zoubin Ghahramani


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Avoiding pathologies in very deep networks


Jul 08, 2016
David Duvenaud, Oren Rippel, Ryan P. Adams, Zoubin Ghahramani

* Fixed a typo regarding number of layers 

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Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation


Jun 19, 2016
Akash Srivastava, James Zou, Ryan P. Adams, Charles Sutton

* presented at 2016 ICML Workshop on Human Interpretability in Machine Learning (WHI 2016), New York, NY 

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