1 code implementation • 3 Oct 2023 • Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet, Kilian Fatras, Jarrid Rector-Brooks, Cheng-Hao Liu, Andrei Cristian Nica, Maksym Korablyov, Michael Bronstein, Alexander Tong
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly.
2 code implementations • 18 Sep 2023 • Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman
Through empirical evaluation across the benchmark, we demonstrate that our approach outperforms deep-learning generation methods in data generation tasks and remains competitive in data imputation.
1 code implementation • 7 Jul 2023 • Alexander Tong, Nikolay Malkin, Kilian Fatras, Lazar Atanackovic, Yanlei Zhang, Guillaume Huguet, Guy Wolf, Yoshua Bengio
We present simulation-free score and flow matching ([SF]$^2$M), a simulation-free objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary source and target distributions.
1 code implementation • 20 Jun 2023 • Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras, Ioannis Mitliagkas
Understanding the optimization dynamics of neural networks is necessary for closing the gap between theory and practice.
no code implementations • 12 Jun 2023 • Thibault Séjourné, Clément Bonet, Kilian Fatras, Kimia Nadjahi, Nicolas Courty
In parallel, unbalanced OT was designed to allow comparisons of more general positive measures, while being more robust to outliers.
no code implementations • 12 Apr 2023 • Alexia Jolicoeur-Martineau, Kilian Fatras, Ke Li, Tal Kachman
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it.
1 code implementation • 6 Apr 2023 • Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras, Yan Zhang, Simon Lacoste-Julien
Based on this idea, we propose PopulAtion Parameter Averaging (PAPA): a method that combines the generality of ensembling with the efficiency of weight averaging.
2 code implementations • 1 Feb 2023 • Alexander Tong, Kilian Fatras, Nikolay Malkin, Guillaume Huguet, Yanlei Zhang, Jarrid Rector-Brooks, Guy Wolf, Yoshua Bengio
CFM features a stable regression objective like that used to train the stochastic flow in diffusion models but enjoys the efficient inference of deterministic flow models.
no code implementations • 3 Oct 2022 • Tiago Salvador, Kilian Fatras, Ioannis Mitliagkas, Adam Oberman
In this work, we consider the Partial Domain Adaptation (PDA) variant, where we have extra source classes not present in the target domain.
no code implementations • 23 Jun 2022 • Kilian Fatras
To decrease the influence of these outliers in the transport problem, we propose to either remove them from the problem or to increase the cost of moving them by using the classifier prediction.
no code implementations • 22 Jun 2022 • Kilian Fatras, Hiroki Naganuma, Ioannis Mitliagkas
It is common in computer vision to be confronted with domain shift: images which have the same class but different acquisition conditions.
2 code implementations • 5 Mar 2021 • Kilian Fatras, Thibault Séjourné, Nicolas Courty, Rémi Flamary
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions.
2 code implementations • 5 Jan 2021 • Kilian Fatras, Younes Zine, Szymon Majewski, Rémi Flamary, Rémi Gribonval, Nicolas Courty
We notably argue that the minibatch strategy comes with appealing properties such as unbiased estimators, gradients and a concentration bound around the expectation, but also with limits: the minibatch OT is not a distance.
no code implementations • 27 Jan 2020 • Jean-Christophe Burnel, Kilian Fatras, Nicolas Courty
In this paper, we present a new method which is able to generate natural adversarial examples from the true data following the second paradigm.
3 code implementations • 9 Oct 2019 • Kilian Fatras, Younes Zine, Rémi Flamary, Rémi Gribonval, Nicolas Courty
Optimal transport distances are powerful tools to compare probability distributions and have found many applications in machine learning.
1 code implementation • 8 Apr 2019 • Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry, Rémi Flamary, Devis Tuia, Nicolas Courty
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping.
1 code implementation • 19 Jun 2018 • Fabian Pedregosa, Kilian Fatras, Mattia Casotto
This is due to the fact that existing methods require to evaluate the proximity operator for the nonsmooth terms, which can be a costly operation for complex penalties.
Optimization and Control 65K10