Search Results for author: Milad Hashemi

Found 16 papers, 8 papers with code

Learning Performance-Improving Code Edits

2 code implementations15 Feb 2023 Aman Madaan, Alexander Shypula, Uri Alon, Milad Hashemi, Parthasarathy Ranganathan, Yiming Yang, Graham Neubig, Amir Yazdanbakhsh

In this paper, we investigate the ability of large language models (LLMs) to suggest functionally correct, performance improving code edits.

Code Generation Code Repair +1

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

1 code implementation1 Nov 2022 Sadegh Mahdavi, Kevin Swersky, Thomas Kipf, Milad Hashemi, Christos Thrampoulidis, Renjie Liao

In this paper, we study the OOD generalization of neural algorithmic reasoning tasks, where the goal is to learn an algorithm (e. g., sorting, breadth-first search, and depth-first search) from input-output pairs using deep neural networks.

Data Augmentation Out-of-Distribution Generalization

CUF: Continuous Upsampling Filters

no code implementations CVPR 2023 Cristina Vasconcelos, Cengiz Oztireli, Mark Matthews, Milad Hashemi, Kevin Swersky, Andrea Tagliasacchi

Neural fields have rapidly been adopted for representing 3D signals, but their application to more classical 2D image-processing has been relatively limited.

Image Super-Resolution

Learning to Improve Code Efficiency

no code implementations9 Aug 2022 Binghong Chen, Daniel Tarlow, Kevin Swersky, Martin Maas, Pablo Heiber, Ashish Naik, Milad Hashemi, Parthasarathy Ranganathan

To automatically learn these hints from the dataset, we propose a novel discrete variational auto-encoder, where each discrete latent variable represents a different learned category of code-edit that increases performance.

Analyzing a Caching Model

no code implementations13 Dec 2021 Leon Sixt, Evan Zheran Liu, Marie Pellat, James Wexler, Milad Hashemi, Been Kim, Martin Maas

Machine Learning has been successfully applied in systems applications such as memory prefetching and caching, where learned models have been shown to outperform heuristics.

Data-Driven Offline Optimization For Architecting Hardware Accelerators

1 code implementation ICLR 2022 Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi, Kevin Swersky, Sergey Levine

An alternative paradigm is to use a "data-driven", offline approach that utilizes logged simulation data, to architect hardware accelerators, without needing any form of simulations.

Computer Architecture and Systems

Oops I Took A Gradient: Scalable Sampling for Discrete Distributions

1 code implementation8 Feb 2021 Will Grathwohl, Kevin Swersky, Milad Hashemi, David Duvenaud, Chris J. Maddison

We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables.

Apollo: Transferable Architecture Exploration

no code implementations2 Feb 2021 Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin, Yanqi Zhou, Albin Jones, Milad Hashemi, Kevin Swersky, Satrajit Chatterjee, Ravi Narayanaswami, James Laudon

We further show that by transferring knowledge between target architectures with different design constraints, Apollo is able to find optimal configurations faster and often with better objective value (up to 25% improvements).

Learned Hardware/Software Co-Design of Neural Accelerators

no code implementations5 Oct 2020 Zhan Shi, Chirag Sakhuja, Milad Hashemi, Kevin Swersky, Calvin Lin

The use of deep learning has grown at an exponential rate, giving rise to numerous specialized hardware and software systems for deep learning.

Bayesian Optimization

An Imitation Learning Approach for Cache Replacement

1 code implementation ICML 2020 Evan Zheran Liu, Milad Hashemi, Kevin Swersky, Parthasarathy Ranganathan, Junwhan Ahn

While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot.

Imitation Learning

Neural Execution Engines: Learning to Execute Subroutines

1 code implementation NeurIPS 2020 Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

A significant effort has been made to train neural networks that replicate algorithmic reasoning, but they often fail to learn the abstract concepts underlying these algorithms.

Learning to Execute


no code implementations ICLR 2020 Yujun Yan, Kevin Swersky, Danai Koutra, Parthasarathy Ranganathan, Milad Hashemi

Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.

Learning Execution through Neural Code Fusion

no code implementations ICLR 2020 Zhan Shi, Kevin Swersky, Daniel Tarlow, Parthasarathy Ranganathan, Milad Hashemi

In this work, we propose a new approach to use GNNs to learn fused representations of general source code and its execution.

Transfer Learning

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