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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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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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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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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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Learning Memory Access Patterns


Mar 06, 2018
Milad Hashemi, Kevin Swersky, Jamie A. Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan


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