5 citations · 6 across the 4 of their papers we have counts for
9 papers
Experimental study of Neural ODE training with adaptive solver for dynamical systems modeling
Alexandre Allauzen, Thiago Petrilli Maffei Dardis, Hannah Plath
Neural Ordinary Differential Equations (ODEs) was recently introduced as a new family of neural network models, which relies on black-box ODE solvers for inference and training. So…
FlauBERT: Unsupervised Language Model Pre-training for French
Hang Le, Loïc Vial, Jibril Frej +7
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks. Leveraging the huge amount of unlabeled texts…
Empirical Study of Diachronic Word Embeddings for Scarce Data
Syrielle Montariol, Alexandre Allauzen
Word meaning change can be inferred from drifts of time-varying word embeddings. However, temporal data may be too sparse to build robust word embeddings and to discriminate signif…
Learning dynamic word embeddings with drift regularisation
Syrielle Montariol, Alexandre Allauzen
Word usage, meaning and connotation change throughout time. Diachronic word embeddings are used to grasp these changes in an unsupervised way. In this paper, we use variants of the…
Exploring sentence informativeness
Syrielle Montariol, Aina Garí Soler, Alexandre Allauzen
This study is a preliminary exploration of the concept of informativeness -how much information a sentence gives about a word it contains- and its potential benefits to building qu…
Control of chaotic systems by Deep Reinforcement Learning
Michele Alessandro Bucci, Onofrio Semeraro, Alexandre Allauzen +3
Deep Reinforcement Learning (DRL) is applied to control a nonlinear, chaotic system governed by the one-dimensional Kuramoto-Sivashinsky (KS) equation. DRL uses reinforcement learn…