| Training on the Edge: The why and the how N Kukreja, A Shilova, O Beaumont, J Huckelheim, N Ferrier, P Hovland, ... 2019 IEEE International Parallel and Distributed Processing Symposium …, 2019 | 35 | 2019 |
| Efficient Combination of Rematerialization and Offloading for Training DNNs O Beaumont, L Eyraud-Dubois, A Shilova Advances in Neural Information Processing Systems 34, 23844-23857, 2021 | 31 | 2021 |
| Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory J Herrmann, O Beaumont, L Eyraud-Dubois, J Hermann, A Joly, A Shilova arXiv preprint arXiv:1911.13214, 2019 | 23 | 2019 |
| Optimal memory-aware backpropagation of deep join networks O Beaumont, J Herrmann, G Pallez, A Shilova Philosophical Transactions of the Royal Society A 378 (2166), 20190049, 2020 | 20 | 2020 |
| Optimal GPU-CPU offloading strategies for deep neural network training O Beaumont, L Eyraud-Dubois, A Shilova European Conference on Parallel Processing, 151-166, 2020 | 16 | 2020 |
| Survey on Efficient Training of Large Neural Networks J Gusak, D Cherniuk, A Shilova, A Katrutsa, D Bershatsky, X Zhao, ... IJCAI-ECAI, 2022 | 15* | 2022 |
| Pipelined model parallelism: Complexity results and memory considerations O Beaumont, L Eyraud-Dubois, A Shilova European Conference on Parallel Processing, 183-198, 2021 | 8 | 2021 |
| A makespan lower bound for the scheduling of the tiled cholesky factorization based on alap schedule O Beaumont, J Langou, W Quach, A Shilova EuroPar 2020-26th International European Conference on Parallel and …, 2020 | 6 | 2020 |
| Revisiting Continuous-Time Reinforcement Learning. A Study of HJB Solvers Based on PINNs and FEMs A Shilova, T Delliaux, P Preux, B Raffin Sixteenth European Workshop on Reinforcement Learning, 2023 | 1 | 2023 |
| MadPipe: Memory Aware Dynamic Programming Algorithm for Pipelined Model Parallelism O Beaumont, L Eyraud-Dubois, A Shilova 2022 IEEE International Parallel and Distributed Processing Symposium …, 2022 | 1 | 2022 |
| Weight Offloading Strategies for Training Large DNN Models O Beaumont, L Eyraud-Dubois, A Shilova, X Zhao | 1 | 2022 |
| An Integer Linear Programming Approach for Pipelined Model Parallelism O Beaumont, L Eyraud-Dubois, A Shilova Inria, 2022 | 1 | 2022 |
| A makespan lower bound for the tiled cholesky factorization based on alap schedule O Beaumont, J Langou, W Quach, A Shilova European Conference on Parallel Processing, 134-150, 2020 | 1 | 2020 |
| Learning HJB Viscosity Solutions with PINNs for Continuous-Time Reinforcement Learning A Shilova, T Delliaux, P Preux, B Raffin Inria Lille-Nord Europe, CRIStAL-Centre de Recherche en Informatique, Signal …, 2024 | | 2024 |
| Optimal Re-Materialization Strategies for Heterogeneous Chains: How to Train Deep Neural Networks with Limited Memory O Beaumont, L Eyraud-Dubois, J Herrmann, A Joly, A Shilova ACM Transactions on Mathematical Software, 2024 | | 2024 |
| AdaStop: sequential testing for efficient and reliable comparisons of Deep RL Agents T Mathieu, R Della Vecchia, A Shilova, MC de Medeiros, H Kohler, ... arXiv preprint arXiv:2306.10882, 2023 | | 2023 |
| Entropy Regularized Reinforcement Learning with Cascading Networks R Della Vecchia, A Shilova, P Preux, R Akrour arXiv preprint arXiv:2210.08503, 2022 | | 2022 |
| Memory Saving Strategies for Deep Neural Network Training A Shilova Université de Bordeaux, 2021 | | 2021 |
| Memory Efficient Deep Neural Network Training A Shilova European Conference on Parallel Processing, 515-519, 2021 | | 2021 |
Olivier BeaumontINRIA Bordeaux Sud-OuestVerified email at labri.fr
