153 citations · 427 across the 46 of their papers we have counts for
12 papers · 1 filter
Kastor: An efficient fine-tuning strategy for generative emulation of PDE simulations
Guillaume Couairon, Alexis Jacq, Yu-Han Wu +4
Machine learning offers a promising avenue to accelerate physical simulations by replacing computationally expensive traditional Partial Differential Equation (PDE) solvers with fa…
Diffusion Fine-tuning with Rewarded Moment Matching Distillation
Alexis Jacq, Guillaume Couairon, Valentin De Bortoli +3
Distillation and Reinforcement Learning (RL) fine-tuning are the primary pillars of diffusion post-training. While traditionally studied in isolation, the interaction between these…
MIND: Monge Inception Distance for Generative Models Evaluation
Quentin Berthet, Yu-Han Wu, Clement Crepy +3
We propose the Monge Inception Distance (MIND), a metric for evaluating generative models that addresses key limitations of the widely adopted Fréchet Inception Distance (FID). The…
Control Variate Score Matching for Diffusion Models
Khaled Kahouli, Romuald Elie, Klaus-Robert Müller +3
Sampling from unnormalized probability densities is a pervasive challenge across the computational and physical sciences. Diffusion models provide a powerful generative framework f…
Language Agents as Digital Representatives in Collective Decision-Making
Daniel Jarrett, Miruna Pîslar, Michiel A. Bakker +6
Consider the process of collective decision-making, in which a group of individuals interactively select a preferred outcome from among a universe of alternatives. In this context,…
Diversity-Rewarded CFG Distillation
Geoffrey Cideron, Andrea Agostinelli, Johan Ferret +5
Generative models are transforming creative domains such as music generation, with inference-time strategies like Classifier-Free Guidance (CFG) playing a crucial role. However, CF…