no code implementations • 13 Mar 2024 • Louis Fournier, Edouard Oyallon
Training large deep learning models requires parallelization techniques to scale.
1 code implementation • 12 Jun 2023 • Louis Fournier, Stéphane Rivaud, Eugene Belilovsky, Michael Eickenberg, Edouard Oyallon
Forward Gradients - the idea of using directional derivatives in forward differentiation mode - have recently been shown to be utilizable for neural network training while avoiding problems generally associated with backpropagation gradient computation, such as locking and memorization requirements.
1 code implementation • EACL 2021 • Louis Fournier, Ewan Dunbar
Many types of distributional word embeddings (weakly) encode linguistic regularities as directions (the difference between "jump" and "jumped" will be in a similar direction to that of "walk" and "walked," and so on).
1 code implementation • CONLL 2020 • Louis Fournier, Emmanuel Dupoux, Ewan Dunbar
Vector space models of words have long been claimed to capture linguistic regularities as simple vector translations, but problems have been raised with this claim.