13 citations · 22 across the 5 of their papers we have counts for
5 papers
AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
Timothée Mathieu, Riccardo Della Vecchia, Alena Shilova +4
Recently, the scientific community has questioned the statistical reproducibility of many empirical results, especially in the field of machine learning. To contribute to the resol…
Entropy Regularized Reinforcement Learning with Cascading Networks
Riccardo Della Vecchia, Alena Shilova, Philippe Preux +1
Deep Reinforcement Learning (Deep RL) has had incredible achievements on high dimensional problems, yet its learning process remains unstable even on the simplest tasks. Deep RL us…
Survey on Large Scale Neural Network Training
Julia Gusak, Daria Cherniuk, Alena Shilova +8
Modern Deep Neural Networks (DNNs) require significant memory to store weight, activations, and other intermediate tensors during training. Hence, many models do not fit one GPU de…
Optimal checkpointing for heterogeneous chains: how to train deep neural networks with limited memory
Julien Herrmann, Olivier Beaumont, Lionel Eyraud-Dubois +3
This paper introduces a new activation checkpointing method which allows to significantly decrease memory usage when training Deep Neural Networks with the back-propagation algorit…
Training on the Edge: The why and the how
Navjot Kukreja, Alena Shilova, Olivier Beaumont +4
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 distrib…