Hashemi, Milad

11 publications

ICLR 2024 Learning Performance-Improving Code Edits Alexander G Shypula, Aman Madaan, Yimeng Zeng, Uri Alon, Jacob R. Gardner, Yiming Yang, Milad Hashemi, Graham Neubig, Parthasarathy Ranganathan, Osbert Bastani, Amir Yazdanbakhsh

CVPR 2023 CUF: Continuous Upsampling Filters Cristina N. Vasconcelos, Cengiz Oztireli, Mark Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi

TMLR 2023 Towards Better Out-of-Distribution Generalization of Neural Algorithmic Reasoning Tasks Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

ICLR 2022 Data-Driven Offline Optimization for Architecting Hardware Accelerators Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine

ICLR 2021 No MCMC for Me: Amortized Sampling for Fast and Stable Training of Energy-Based Models Will Sussman Grathwohl, Jacob Jin Kelly, Milad Hashemi, Mohammad Norouzi, Kevin Swersky, David Duvenaud

ICML 2021 Oops I Took a Gradient: Scalable Sampling for Discrete Distributions Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris Maddison

ICML 2020 An Imitation Learning Approach for Cache Replacement Evan Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

ICLR 2020 Learning Execution Through Neural Code Fusion Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

ICLR 2020 Neural Execution Engines Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

NeurIPS 2020 Neural Execution Engines: Learning to Execute Subroutines Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

ICML 2018 Learning Memory Access Patterns Milad Hashemi, Kevin Swersky, Jamie Smith, Grant Ayers, Heiner Litz, Jichuan Chang, Christos Kozyrakis, Parthasarathy Ranganathan