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Data-Driven Offline Optimization For Architecting Hardware Accelerators

Oct 20, 2021
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine

* First two authors contributed equally 

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Automatic prior selection for meta Bayesian optimization with a case study on tuning deep neural network optimizers

Sep 16, 2021
Zi Wang, George E. Dahl, Kevin Swersky, Chansoo Lee, Zelda Mariet, Zack Nado, Justin Gilmer, Jasper Snoek, Zoubin Ghahramani

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Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks

Feb 24, 2021
Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, Danai Koutra

* 8 pages main paper + 7 pages appendix 

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Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

Feb 08, 2021
Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

* Energy-Based Models, Deep generative models, MCMC sampling 

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Apollo: Transferable Architecture Exploration

Feb 02, 2021
Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon

* 10 pages, 5 figures, Accepted to Workshop on ML for Systems at the 34th Conference on Neural Information Processing Systems (NeurIPS 2020) 

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Human 3D keypoints via spatial uncertainty modeling

Dec 18, 2020
Francis Williams, Or Litany, Avneesh Sud, Kevin Swersky, Andrea Tagliasacchi

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No MCMC for me: Amortized sampling for fast and stable training of energy-based models

Oct 14, 2020
Will Grathwohl, Jacob Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud

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Learned Hardware/Software Co-Design of Neural Accelerators

Oct 05, 2020
Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin

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Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach

Aug 18, 2020
Martin Mladenov, Elliot Creager, Omer Ben-Porat, Kevin Swersky, Richard Zemel, Craig Boutilier

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An Imitation Learning Approach for Cache Replacement

Jul 09, 2020
Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

* International Conference on Machine Learning (ICML), 2020 

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Neural Execution Engines: Learning to Execute Subroutines

Jun 23, 2020
Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

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Big Self-Supervised Models are Strong Semi-Supervised Learners

Jun 17, 2020
Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey Hinton

* code and pretrained models at 

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SentenceMIM: A Latent Variable Language Model

Mar 06, 2020
Micha Livne, Kevin Swersky, David J. Fleet

* Preprint 

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Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One

Dec 11, 2019
Will Grathwohl, Kuan-Chieh Wang, Jörn-Henrik Jacobsen, David Duvenaud, Mohammad Norouzi, Kevin Swersky

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MIM: Mutual Information Machine

Oct 14, 2019
Micha Livne, Kevin Swersky, David J. Fleet

* Pre-print. Project webpage: 

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High Mutual Information in Representation Learning with Symmetric Variational Inference

Oct 04, 2019
Micha Livne, Kevin Swersky, David J. Fleet

* Bayesian Deep Learning Workshop (NeurIPS 2019). arXiv admin note: substantial text overlap with arXiv:1910.03175 

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Learning Execution through Neural Code Fusion

Jun 17, 2019
Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

* 14 pages,7 figures 

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Flexibly Fair Representation Learning by Disentanglement

Jun 06, 2019
Elliot Creager, David Madras, Jörn-Henrik Jacobsen, Marissa A. Weis, Kevin Swersky, Toniann Pitassi, Richard Zemel

* Proceedings of the International Conference on Machine Learning (ICML), 2019 

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Learning Sparse Networks Using Targeted Dropout

Jun 05, 2019
Aidan N. Gomez, Ivan Zhang, Kevin Swersky, Yarin Gal, Geoffrey E. Hinton

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Graph Normalizing Flows

May 30, 2019
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamie Kiros, Kevin Swersky

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Neural Networks for Modeling Source Code Edits

Apr 04, 2019
Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow

* Deanonymized version of ICLR 2019 submission 

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