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Benoit Steiner
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2020 – today
- 2023
- [c14]Taoan Huang
, Aaron M. Ferber
Local Branching Relaxation Heuristics for Integer Linear Programs. CPAIOR 2023: 96-113 - [c13]Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian:
SurCo: Learning Linear SURrogates for COmbinatorial Nonlinear Optimization Problems. ICML 2023: 10034-10052 - [c12]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. ICML 2023: 13869-13890 - [c11]Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh James Leather, Yuandong Tian:
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction. ICML 2023: 20746-20762 - [c10]Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty:
MODeL: Memory Optimizations for Deep Learning. ICML 2023: 32618-32632 - [i17]Youwei Liang, Kevin Stone, Ali Shameli, Chris Cummins, Mostafa Elhoushi, Jiadong Guo, Benoit Steiner, Xiaomeng Yang, Pengtao Xie, Hugh Leather, Yuandong Tian:
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction. CoRR abs/2301.05104 (2023) - [i16]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Searching Large Neighborhoods for Integer Linear Programs with Contrastive Learning. CoRR abs/2302.01578 (2023) - [i15]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:
Measuring Faithfulness in Chain-of-Thought Reasoning. CoRR abs/2307.13702 (2023) - [i14]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:
Studying Large Language Model Generalization with Influence Functions. CoRR abs/2308.03296 (2023) - 2022
- [c9]Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather:
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research. CGO 2022: 92-105 - [c8]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:
Flashlight: Enabling Innovation in Tools for Machine Learning. ICML 2022: 10557-10574 - [c7]Shikhar Singh, James Hegarty, Hugh Leather, Benoit Steiner:
A graph neural network-based performance model for deep learning applications. MAPS@PLDI 2022: 11-20 - [i13]Jacob 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:
Flashlight: Enabling Innovation in Tools for Machine Learning. CoRR abs/2201.12465 (2022) - [i12]Bram Wasti, José Pablo Cambronero, Benoit Steiner, Hugh Leather, Aleksandar Zlateski:
LoopStack: a Lightweight Tensor Algebra Compiler Stack. CoRR abs/2205.00618 (2022) - [i11]Aaron M. Ferber, Taoan Huang, Daochen Zha, Martin Schubert, Benoit Steiner, Bistra Dilkina, Yuandong Tian:
SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problems. CoRR abs/2210.12547 (2022) - [i10]Benoit Steiner, Mostafa Elhoushi, Jacob Kahn, James Hegarty:
OLLA: Decreasing the Memory Usage of Neural Networks by Optimizing the Lifetime and Location of Arrays. CoRR abs/2210.12924 (2022) - [i9]Taoan Huang, Aaron M. Ferber, Yuandong Tian, Bistra Dilkina, Benoit Steiner:
Local Branching Relaxation Heuristics for Integer Linear Programs. CoRR abs/2212.08183 (2022) - 2021
- [c6]Benoit Steiner, Chris Cummins, Horace He, Hugh Leather:
Value Learning for Throughput Optimization of Deep Learning Workloads. MLSys 2021 - [c5]Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian:
Learning Space Partitions for Path Planning. NeurIPS 2021: 378-391 - [i8]Kevin Yang, Tianjun Zhang, Chris Cummins, Brandon Cui, Benoit Steiner, Linnan Wang, Joseph E. Gonzalez, Dan Klein, Yuandong Tian:
Learning Space Partitions for Path Planning. CoRR abs/2106.10544 (2021) - [i7]Shikhar Singh, Benoit Steiner, James Hegarty, Hugh Leather:
Using Graph Neural Networks to model the performance of Deep Neural Networks. CoRR abs/2108.12489 (2021) - [i6]Chris Cummins, Bram Wasti, Jiadong Guo, Brandon Cui, Jason Ansel, Sahir Gomez, Somya Jain, Jia Liu, Olivier Teytaud, Benoit Steiner, Yuandong Tian, Hugh Leather:
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research. CoRR abs/2109.08267 (2021) - 2020
- [i5]Benoit Steiner, Chris Cummins, Horace He, Hugh Leather:
Value Function Based Performance Optimization of Deep Learning Workloads. CoRR abs/2011.14486 (2020)
2010 – 2019
- 2019
- [j1]Andrew Adams, Karima Ma, Luke Anderson, Riyadh Baghdadi, Tzu-Mao Li, Michaël Gharbi, Benoit Steiner, Steven Johnson, Kayvon Fatahalian, Frédo Durand, Jonathan Ragan-Kelley:
Learning to optimize halide with tree search and random programs. ACM Trans. Graph. 38(4): 121:1-121:12 (2019) - [c4]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:
PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS 2019: 8024-8035 - [i4]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, Zach DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, Soumith Chintala:
PyTorch: An Imperative Style, High-Performance Deep Learning Library. CoRR abs/1912.01703 (2019) - 2018
- [c3]Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean:
A Hierarchical Model for Device Placement. ICLR (Poster) 2018 - 2017
- [c2]Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean:
Device Placement Optimization with Reinforcement Learning. ICML 2017: 2430-2439 - [i3]Azalia Mirhoseini, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, Jeff Dean:
Device Placement Optimization with Reinforcement Learning. CoRR abs/1706.04972 (2017) - 2016
- [c1]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:
TensorFlow: A System for Large-Scale Machine Learning. OSDI 2016: 265-283 - [i2]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:
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems. CoRR abs/1603.04467 (2016) - [i1]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:
TensorFlow: A system for large-scale machine learning. CoRR abs/1605.08695 (2016)
Coauthor Index
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