13 citations · 13 across the 3 of their papers we have counts for
3 papers
Two-level domain-decomposition AdaGrad method for scalable training of graph neural networks
Laurynas Varnas, Julien Herrmann, Alexander Heinlein +2
Graph neural networks (GNNs) have emerged as a powerful framework for learning from graph-structured data. However, their efficient training remains challenging, particularly in di…
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…
Designing LU-QR hybrid solvers for performance and stability
Mathieu Faverge, Julien Herrmann, Julien Langou +3
This paper introduces hybrid LU-QR al- gorithms for solving dense linear systems of the form Ax = b. Throughout a matrix factorization, these al- gorithms dynamically alternate LU…