47 citations · 94 across the 5 of their papers we have counts for
5 papers
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier +1
Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data. Recently, deep neural models trained on procedurally-generated synthetic datasets sho…
End-to-end symbolic regression with transformers
Pierre-Alexandre Kamienny, Stéphane d'Ascoli, Guillaume Lample +1
Symbolic regression, the task of predicting the mathematical expression of a function from the observation of its values, is a difficult task which usually involves a two-step proc…
Deep Symbolic Regression for Recurrent Sequences
Stéphane d'Ascoli, Pierre-Alexandre Kamienny, Guillaume Lample +1
Symbolic regression, i.e. predicting a function from the observation of its values, is well-known to be a challenging task. In this paper, we train Transformers to infer the functi…
Learning advanced mathematical computations from examples
François Charton, Amaury Hayat, Guillaume Lample
Using transformers over large generated datasets, we train models to learn mathematical properties of differential systems, such as local stability, behavior at infinity and contro…
Deep Learning for Symbolic Mathematics
Guillaume Lample, François Charton
Neural networks have a reputation for being better at solving statistical or approximate problems than at performing calculations or working with symbolic data. In this paper, we s…