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Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks


Nov 01, 2022
Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

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CUF: Continuous Upsampling Filters


Oct 20, 2022
Cristina Vasconcelos, Cengiz Oztireli, Mark Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi

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Learning to Improve Code Efficiency


Aug 09, 2022
Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan

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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

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* First two authors contributed equally 

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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

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* 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

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* 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

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* 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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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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