no code implementations • 20 Jul 2023 • Raphael Boige, Yannis Flet-Berliac, Arthur Flajolet, Guillaume Richard, Thomas Pierrot
Self-supervised learning has brought about a revolutionary paradigm shift in various computing domains, including NLP, vision, and biology.
no code implementations • 8 Jun 2023 • Raphael Boige, Guillaume Richard, Jérémie Dona, Thomas Pierrot, Antoine Cully
While early QD algorithms view the objective and descriptor functions as black-box functions, novel tools have been introduced to use gradient information to accelerate the search and improve overall performance of those algorithms over continuous input spaces.
no code implementations • 27 Mar 2023 • Valentin Macé, Raphaël Boige, Felix Chalumeau, Thomas Pierrot, Guillaume Richard, Nicolas Perrin-Gilbert
In the context of neuroevolution, Quality-Diversity algorithms have proven effective in generating repertoires of diverse and efficient policies by relying on the definition of a behavior space.
1 code implementation • 7 Feb 2022 • Thomas Pierrot, Guillaume Richard, Karim Beguir, Antoine Cully
In this work, we consider the problem of Quality-Diversity (QD) optimization with multiple objectives.
1 code implementation • 28 Dec 2021 • Sankalp Gilda, Antoine de Mathelin, Sabine Bellstedt, Guillaume Richard
The prevalent paradigm of machine learning today is to use past observations to predict future ones.
1 code implementation • 7 Jul 2021 • Antoine de Mathelin, Mounir Atiq, Guillaume Richard, Alejandro de la Concha, Mouad Yachouti, François Deheeger, Mathilde Mougeot, Nicolas Vayatis
In this paper, we introduce the ADAPT library, an open source Python API providing the implementation of the main transfer learning and domain adaptation methods.
2 code implementations • 15 Jun 2020 • Antoine de Mathelin, Guillaume Richard, Francois Deheeger, Mathilde Mougeot, Nicolas Vayatis
We present a novel instance-based approach to handle regression tasks in the context of supervised domain adaptation under an assumption of covariate shift.
no code implementations • 10 Feb 2020 • Guillaume Richard, Benoît Grossin, Guillaume Germaine, Georges Hébrail, Anne de Moliner
Time series clustering is a challenging task due to the specific nature of the data.