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ORCIDAlban Desmaison
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2020 – today
- 2024
[c13]Dongning Ma
, Fan Fred Lin, Alban Desmaison, Joel Coburn, Daniel Moore, Sriram Sankar, Xun Jiao:
Dr. DNA: Combating Silent Data Corruptions in Deep Learning using Distribution of Neuron Activations. ASPLOS (3) 2024: 239-252- [c12]Jason Ansel, Edward Z. Yang, Horace He, Natalia Gimelshein, Animesh Jain, Michael Voznesensky, Bin Bao, Peter Bell, David Berard, Evgeni Burovski, Geeta Chauhan, Anjali Chourdia, Will Constable, Alban Desmaison, Zachary DeVito, Elias Ellison, Will Feng, Jiong Gong, Michael Gschwind, Brian Hirsh, Sherlock Huang, Kshiteej Kalambarkar, Laurent Kirsch, Michael Lazos, Mario Lezcano, Yanbo Liang, Jason Liang, Yinghai Lu, C. K. Luk, Bert Maher, Yunjie Pan, Christian Puhrsch, Matthias Reso, Mark Saroufim, Marcos Yukio Siraichi, Helen Suk, Shunting Zhang, Michael Suo, Phil Tillet, Xu Zhao, Eikan Wang, Keren Zhou, Richard Zou, Xiaodong Wang, Ajit Mathews, William Wen, Gregory Chanan, Peng Wu, Soumith Chintala:
PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation. ASPLOS (2) 2024: 929-947 - 2023
- [j2]Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Pritam Damania, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Ajit Mathews, Shen Li:
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel. Proc. VLDB Endow. 16(12): 3848-3860 (2023) - [c11]Shen Li, Pritam Damania, Luca Wehrstedt, Rohan Varma, Omkar Salpekar, Pavel Belevich, Howard Huang, Yanli Zhao, Lucas Hosseini, Wanchao Liang, Hongyi Jia, Shihao Xu, Satendra Gera, Alisson G. Azzolini, Guoqiang Jerry Chen, Zachary DeVito, Chaoyang He, Amir Ziashahabi, Alban Desmaison, Edward Z. Yang, Gregory Chanan, Brian Vaughan, Manoj Krishnan, Joseph S. Spisak, Salman Avestimehr, Soumith Chintala:
PyTorch RPC: Distributed Deep Learning Built on Tensor-Optimized Remote Procedure Calls. MLSys 2023 - [c10]Xun Jiao, Fan Fred Lin, Matt Xiao, Alban Desmaison, Daniel Moore, Sriram Sankar:
Brief Industry Paper: Evaluating Robustness of Deep Learning-Based Recommendation Systems Against Hardware Errors: A Case Study. RTSS 2023: 468-472 - [i16]Yanli Zhao, Andrew Gu, Rohan Varma, Liang Luo, Chien-Chin Huang, Min Xu, Less Wright, Hamid Shojanazeri, Myle Ott, Sam Shleifer, Alban Desmaison, Can Balioglu, Bernard Nguyen, Geeta Chauhan, Yuchen Hao, Shen Li:
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel. CoRR abs/2304.11277 (2023) - [i15]Dongning Ma, Xun Jiao, Fan Fred Lin, Mengshi Zhang, Alban Desmaison, Thomas Sellinger, Daniel Moore, Sriram Sankar:
Evaluating and Enhancing Robustness of Deep Recommendation Systems Against Hardware Errors. CoRR abs/2307.10244 (2023) - 2021
- [i14]Alessandro De Palma, Rudy Bunel, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition. CoRR abs/2104.06718 (2021) - [i13]Frédéric Magoulès, Abal-Kassim Cheik Ahamed, Alban Desmaison, Jean-Christophe Léchenet, François Mayer, Haifa Ben Salem, Thomas Zhu:
Power Consumption Analysis of Parallel Algorithms on GPUs. CoRR abs/2110.01414 (2021) - [i12]Abal-Kassim Cheik Ahamed, Alban Desmaison, Frédéric Magoulès:
Fast and Green Computing with Graphics Processing Units for solving Sparse Linear Systems. CoRR abs/2112.10823 (2021) - 2020
- [c9]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. UAI 2020: 370-379 - [i11]Rudy Bunel, Alessandro De Palma, Alban Desmaison, Krishnamurthy Dvijotham, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Lagrangian Decomposition for Neural Network Verification. CoRR abs/2002.10410 (2020)
2010 – 2019
- 2019
- [b1]Alban Desmaison:
Optimization for, and by, machine learning. University of Oxford, UK, 2019 - [j1]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher-Order Potentials. SIAM J. Imaging Sci. 12(1): 287-318 (2019) - [c8]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 - [i10]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
- [i9]Thomas Joy, Alban Desmaison, Thalaiyasingam Ajanthan, Rudy Bunel, Mathieu Salzmann, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials. CoRR abs/1805.09028 (2018) - 2017
- [c7]Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar:
Efficient Linear Programming for Dense CRFs. CVPR 2017: 2934-2942 - [c6]Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs. ICLR (Poster) 2017 - [c5]Siddharth Narayanaswamy, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Noah D. Goodman, Pushmeet Kohli, Frank D. Wood, Philip H. S. Torr:
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. NIPS 2017: 5925-5935 - [i8]N. Siddharth, Brooks Paige, Jan-Willem van de Meent, Alban Desmaison, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr:
Learning Disentangled Representations with Semi-Supervised Deep Generative Models. CoRR abs/1706.00400 (2017) - 2016
- [c4]Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Continuous Relaxations for Dense CRF. ECCV (2) 2016: 818-833 - [c3]Rudy Bunel, Alban Desmaison, Pawan Kumar Mudigonda, Pushmeet Kohli, Philip H. S. Torr:
Adaptive Neural Compilation. NIPS 2016: 1444-1452 - [i7]Rudy Bunel, Alban Desmaison, Pushmeet Kohli, Philip H. S. Torr, Pawan Kumar Mudigonda:
Adaptive Neural Compilation. CoRR abs/1605.07969 (2016) - [i6]Alban Desmaison, Rudy Bunel, Pushmeet Kohli, Philip H. S. Torr, M. Pawan Kumar:
Efficient Continuous Relaxations for Dense CRF. CoRR abs/1608.06192 (2016) - [i5]Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs. CoRR abs/1611.01787 (2016) - [i4]N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank D. Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr:
Inducing Interpretable Representations with Variational Autoencoders. CoRR abs/1611.07492 (2016) - [i3]Thalaiyasingam Ajanthan, Alban Desmaison, Rudy Bunel, Mathieu Salzmann, Philip H. S. Torr, M. Pawan Kumar:
Efficient Linear Programming for Dense CRFs. CoRR abs/1611.09718 (2016) - [i2]Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N. Siddharth, Philip H. S. Torr:
Playing Doom with SLAM-Augmented Deep Reinforcement Learning. CoRR abs/1612.00380 (2016) - [i1]Rudy Bunel, Alban Desmaison, M. Pawan Kumar, Philip H. S. Torr, Pushmeet Kohli:
Learning to superoptimize programs - Workshop Version. CoRR abs/1612.01094 (2016) - 2014
- [c2]Abal-Kassim Cheik Ahamed, Alban Desmaison, Frédéric Magoulès:
Fast and Green Computing with Graphics Processing Units for Solving Sparse Linear Systems. HPCC/CSS/ICESS 2014: 129-136 - [c1]Frédéric Magoulès, Abal-Kassim Cheik Ahamed, Alban Desmaison, Jean-Christophe Léchenet, François Mayer, Haifa Ben Salem, Thomas Zhu:
Power Consumption Analysis of Parallel Algorithms on GPUs. HPCC/CSS/ICESS 2014: 304-311
Coauthor Index
aka: Philip H. S. Torr
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