no code implementations • 19 Jun 2023 • Timothée Mathieu, Riccardo Della Vecchia, Alena Shilova, Matheus Medeiros Centa, Hector Kohler, Odalric-Ambrym Maillard, Philippe Preux
When comparing several RL algorithms, a major question is how many executions must be made and how can we ensure that the results of such a comparison are theoretically sound.
no code implementations • 16 Oct 2022 • Riccardo Della Vecchia, Alena Shilova, Philippe Preux, Riad Akrour
Compared to these learning frameworks, one of the major difficulties of RL is the absence of i. i. d.
no code implementations • 21 Feb 2022 • Julia Gusak, Daria Cherniuk, Alena Shilova, Alexander Katrutsa, Daniel Bershatsky, Xunyi Zhao, Lionel Eyraud-Dubois, Oleg Shlyazhko, Denis Dimitrov, Ivan Oseledets, Olivier Beaumont
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training.
no code implementations • NeurIPS 2021 • Olivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova
Rematerialization and offloading are two well known strategies to save memory during the training phase of deep neural networks, allowing data scientists to consider larger models, batch sizes or higher resolution data.
no code implementations • 27 Nov 2019 • Julien Herrmann, Olivier Beaumont, Lionel Eyraud-Dubois, Julien Hermann, Alexis Joly, Alena Shilova
This paper introduces a new activation checkpointing method which allows to significantly decrease memory usage when training Deep Neural Networks with the back-propagation algorithm.
no code implementations • 13 Feb 2019 • Navjot Kukreja, Alena Shilova, Olivier Beaumont, Jan Huckelheim, Nicola Ferrier, Paul Hovland, Gerard Gorman
Edge computing is the natural progression from Cloud computing, where, instead of collecting all data and processing it centrally, like in a cloud computing environment, we distribute the computing power and try to do as much processing as possible, close to the source of the data.
Distributed, Parallel, and Cluster Computing