Benoit Steiner

According to our database, Benoit Steiner authored at least 23 papers between 2016 and 2023.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of four.

2023

Studying Large Language Model Generalization with Influence Functions.

Roger B. Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamile Lukosiute, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, Samuel R. Bowman

CoRR, 2023

Measuring Faithfulness in Chain-of-Thought Reasoning.

Tamera Lanham, Anna Chen, Ansh Radhakrishnan, Benoit Steiner, Carson Denison, Danny Hernandez, Dustin Li, Esin Durmus, Evan Hubinger, Jackson Kernion, Kamile Lukosiute, Karina Nguyen, Newton Cheng, Nicholas Joseph, Nicholas Schiefer, Oliver Rausch, Robin Larson, Sam McCandlish, Sandipan Kundu, Saurav Kadavath, Shannon Yang, Thomas Henighan, Timothy Maxwell, Timothy Telleen-Lawton, Tristan Hume, Zac Hatfield-Dodds, Jared Kaplan, Jan Brauner, Samuel R. Bowman, Ethan Perez

CoRR, 2023

MODeL: Memory Optimizations for Deep Learning.

Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty

Proceedings of the International Conference on Machine Learning, 2023

Learning Compiler Pass Orders using Coreset and Normalized Value Prediction.

Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh James Leather, Yuandong Tian

Proceedings of the International Conference on Machine Learning, 2023

Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning.

Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner

Proceedings of the International Conference on Machine Learning, 2023

SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems.

Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian

Proceedings of the International Conference on Machine Learning, 2023

Local Branching Relaxation Heuristics for Integer Linear Programs.

Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner

Proceedings of the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, 2023

2022

OLLA: Decreasing the Memory Usage of Neural Networks by Optimizing the Lifetime and Location of Arrays.

Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty

CoRR, 2022

LoopStack: a Lightweight Tensor Algebra Compiler Stack.

Bram Wasti, José Pablo Cambronero, Benoit Steiner, Hugh Leather, Aleksandar Zlateski

CoRR, 2022

A graph neural network-based performance model for deep learning applications.

Shikhar Singh, James Hegarty, Hugh Leather, Benoit Steiner

Proceedings of the MAPS@PLDI 2022: 6th ACM SIGPLAN International Symposium on Machine Programming, 2022

Flashlight: Enabling Innovation in Tools for Machine Learning.

Jacob D. Kahn, Vineel Pratap, Tatiana Likhomanenko, Qiantong Xu, Awni Y. Hannun, Jeff Cai, Paden Tomasello, Ann Lee, Edouard Grave, Gilad Avidov, Benoit Steiner, Vitaliy Liptchinsky, Gabriel Synnaeve, Ronan Collobert

Proceedings of the International Conference on Machine Learning, 2022

CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research.

Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather

Proceedings of the IEEE/ACM International Symposium on Code Generation and Optimization, 2022

2021

Using Graph Neural Networks to model the performance of Deep Neural Networks.

Shikhar Singh, Benoit Steiner, James Hegarty, Hugh Leather

CoRR, 2021

Learning Space Partitions for Path Planning.

Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Value Learning for Throughput Optimization of Deep Learning Workloads.

Benoit Steiner, Chris Cummins, Horace He, Hugh Leather

Proceedings of Machine Learning and Systems 2021, 2021

2020

Value Function Based Performance Optimization of Deep Learning Workloads.

Benoit Steiner, Chris Cummins, Horace He, Hugh Leather

CoRR, 2020

2019

Learning to optimize halide with tree search and random programs.

Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley

ACM Trans. Graph., 2019

PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Köpf, Edward Z. Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

A Hierarchical Model for Device Placement.

Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Device Placement Optimization with Reinforcement Learning.

Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

TensorFlow: A system for large-scale machine learning.

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zhang

CoRR, 2016

TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

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

CoRR, 2016

TensorFlow: A System for Large-Scale Machine Learning.

Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek Gordon Murray, Benoit Steiner, Paul A. Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng

Proceedings of the 12th USENIX Symposium on Operating Systems Design and Implementation, 2016