Kepler: Robust Learning for Faster Parametric Query Optimization

no code implementations • 11 Jun 2023 • Lyric Doshi, Vincent Zhuang, Gaurav Jain, Ryan Marcus, Haoyu Huang, Deniz Altınbüken, Eugene Brevdo, Campbell Fraser

We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer.

A Transferable Approach for Partitioning Machine Learning Models on Multi-Chip-Modules

no code implementations • 7 Dec 2021 • Xinfeng Xie, Prakash Prabhu, Ulysse Beaugnon, Phitchaya Mangpo Phothilimthana, Sudip Roy, Azalia Mirhoseini, Eugene Brevdo, James Laudon, Yanqi Zhou

Partitioning ML graphs for MCMs is particularly hard as the search space grows exponentially with the number of chiplets available and the number of nodes in the neural network.

BIG-bench Machine Learning Reinforcement Learning (RL)

Differentiable Architecture Search for Reinforcement Learning

1 code implementation • 4 Jun 2021 • Yingjie Miao, Xingyou Song, John D. Co-Reyes, Daiyi Peng, Summer Yue, Eugene Brevdo, Aleksandra Faust

In this paper, we investigate the fundamental question: To what extent are gradient-based neural architecture search (NAS) techniques applicable to RL?

Neural Architecture Search reinforcement-learning +1

Reverb: A Framework For Experience Replay

1 code implementation • 9 Feb 2021 • Albin Cassirer, Gabriel Barth-Maron, Eugene Brevdo, Sabela Ramos, Toby Boyd, Thibault Sottiaux, Manuel Kroiss

A central component of training in Reinforcement Learning (RL) is Experience: the data used for training.

Reinforcement Learning (RL)

MLGO: a Machine Learning Guided Compiler Optimizations Framework

1 code implementation • 13 Jan 2021 • Mircea Trofin, Yundi Qian, Eugene Brevdo, Zinan Lin, Krzysztof Choromanski, David Li

Leveraging machine-learning (ML) techniques for compiler optimizations has been widely studied and explored in academia.

BIG-bench Machine Learning

Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansion

2 code implementations • NeurIPS 2018 • Jacob Buckman, Danijar Hafner, George Tucker, Eugene Brevdo, Honglak Lee

Integrating model-free and model-based approaches in reinforcement learning has the potential to achieve the high performance of model-free algorithms with low sample complexity.

Continuous Control reinforcement-learning +1

Dynamic Control Flow in Large-Scale Machine Learning

no code implementations • 4 May 2018 • Yuan Yu, Martín Abadi, Paul Barham, Eugene Brevdo, Mike Burrows, Andy Davis, Jeff Dean, Sanjay Ghemawat, Tim Harley, Peter Hawkins, Michael Isard, Manjunath Kudlur, Rajat Monga, Derek Murray, Xiaoqiang Zheng

We describe the design of the programming model, and its implementation in TensorFlow, a distributed machine learning system.

BIG-bench Machine Learning

Tensor2Tensor for Neural Machine Translation

14 code implementations • WS 2018 • Ashish Vaswani, Samy Bengio, Eugene Brevdo, Francois Chollet, Aidan N. Gomez, Stephan Gouws, Llion Jones, Łukasz Kaiser, Nal Kalchbrenner, Niki Parmar, Ryan Sepassi, Noam Shazeer, Jakob Uszkoreit

Tensor2Tensor is a library for deep learning models that is well-suited for neural machine translation and includes the reference implementation of the state-of-the-art Transformer model.

Machine Translation Translation

TensorFlow Distributions

9 code implementations • 28 Nov 2017 • Joshua V. Dillon, Ian Langmore, Dustin Tran, Eugene Brevdo, Srinivas Vasudevan, Dave Moore, Brian Patton, Alex Alemi, Matt Hoffman, Rif A. Saurous

The TensorFlow Distributions library implements a vision of probability theory adapted to the modern deep-learning paradigm of end-to-end differentiable computation.

Probabilistic Programming

Deep Probabilistic Programming

no code implementations • 13 Jan 2017 • Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei

By treating inference as a first class citizen, on a par with modeling, we show that probabilistic programming can be as flexible and computationally efficient as traditional deep learning.

Probabilistic Programming Variational Inference

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

4 code implementations • 14 Mar 2016 • Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S. Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Ian Goodfellow, Andrew Harp, Geoffrey Irving, Michael Isard, Yangqing Jia, Rafal Jozefowicz, Lukasz Kaiser, Manjunath Kudlur, Josh Levenberg, Dan Mane, Rajat Monga, Sherry Moore, Derek Murray, Chris Olah, Mike Schuster, Jonathon Shlens, Benoit Steiner, Ilya Sutskever, Kunal Talwar, Paul Tucker, Vincent Vanhoucke, Vijay Vasudevan, Fernanda Viegas, Oriol Vinyals, Pete Warden, Martin Wattenberg, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.

BIG-bench Machine Learning Clustering +4