34 citations · 162 across the 17 of their papers we have counts for
12 papers · 1 filter
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Guillaume Huguet, James Vuckovic, Kilian Fatras +9
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences…
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
Avishek Joey Bose, Tara Akhound-Sadegh, Guillaume Huguet +7
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly. Toward this goal, we introduce FoldFlow, a series of novel…
Generating and Imputing Tabular Data via Diffusion and Flow-based Gradient-Boosted Trees
Alexia Jolicoeur-Martineau, Kilian Fatras, Tal Kachman
Tabular data is hard to acquire and is subject to missing values. This paper introduces a novel approach for generating and imputing mixed-type (continuous and categorical) tabular…
Simulation-free Schrödinger bridges via score and flow matching
Alexander Tong, Nikolay Malkin, Kilian Fatras +5
We present simulation-free score and flow matching ([SF]M), a simulation-free objective for inferring stochastic dynamics given unpaired samples drawn from arbitrary source and…
No Wrong Turns: The Simple Geometry Of Neural Networks Optimization Paths
Charles Guille-Escuret, Hiroki Naganuma, Kilian Fatras +1
Understanding the optimization dynamics of neural networks is necessary for closing the gap between theory and practice. Stochastic first-order optimization algorithms are known to…
Slicing Unbalanced Optimal Transport
Clément Bonet, Kimia Nadjahi, Thibault Séjourné +2
Optimal transport (OT) is a powerful framework to compare probability measures, a fundamental task in many statistical and machine learning problems. Substantial advances have been…
Diffusion models with location-scale noise
Alexia Jolicoeur-Martineau, Kilian Fatras, Ke Li +1
Diffusion Models (DMs) are powerful generative models that add Gaussian noise to the data and learn to remove it. We wanted to determine which noise distribution (Gaussian or non-G…
PopulAtion Parameter Averaging (PAPA)
Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras +2
Ensemble methods combine the predictions of multiple models to improve performance, but they require significantly higher computation costs at inference time. To avoid these costs,…
Improving and generalizing flow-based generative models with minibatch optimal transport
Alexander Tong, Kilian Fatras, Nikolay Malkin +5
Continuous normalizing flows (CNFs) are an attractive generative modeling technique, but they have been held back by limitations in their simulation-based maximum likelihood traini…
Unbalanced minibatch Optimal Transport; applications to Domain Adaptation
Kilian Fatras, Thibault Séjourné, Nicolas Courty +1
Optimal transport distances have found many applications in machine learning for their capacity to compare non-parametric probability distributions. Yet their algorithmic complexit…
Generating Natural Adversarial Hyperspectral examples with a modified Wasserstein GAN
Jean-Christophe Burnel, Kilian Fatras, Nicolas Courty
Adversarial examples are a hot topic due to their abilities to fool a classifier's prediction. There are two strategies to create such examples, one uses the attacked classifier's…
Wasserstein Adversarial Regularization (WAR) on label noise
Kilian Fatras, Bharath Bhushan Damodaran, Sylvain Lobry +3
Noisy labels often occur in vision datasets, especially when they are obtained from crowdsourcing or Web scraping. We propose a new regularization method, which enables learning ro…