3 citations · 5 across the 4 of their papers we have counts for
6 papers
Global Exponential Convergence of Two-Layer Linear Network Training
Stephen Y Zhang, Gabriel Peyré
We prove global exponential (linear) convergence with an explicit rate in the rich scaling for wide two-layer linear networks trained with smooth Polyak-Lojasiewicz predictor losse…
World Action Modeling with Progressive Visual Planning
Fei Zhang, Zhaochong An, Duncan Frost +5
World action models (WAMs) have emerged as a promising paradigm for robotic control by jointly predicting future visual dynamics and actions from an initial observation and instruc…
The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL
Nicolas Beltran-Velez, Felix Friedrich, Zhang Xiaofeng +4
Score- and flow-matching models often rely on preference-based reinforcement learning for two purposes: aligning with subjective preferences and, surprisingly, recovering propertie…
PGT: Procedurally Generated Tasks for improving visual grounding in MLLMs
Rim Assouel, Amir Bar, Michal Drozdzal +1
Despite remarkable progress in Multimodal Large Language Models (MLLMs), these models still struggle with fine-grained understanding tasks. In this work, we propose Procedurally Ge…
Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control
Carles Domingo-Enrich, Michal Drozdzal, Brian Karrer +1
Dynamical generative models that produce samples through an iterative process, such as Flow Matching and denoising diffusion models, have seen widespread use, but there have not be…
InCoRo: In-Context Learning for Robotics Control with Feedback Loops
Jiaqiang Ye Zhu, Carla Gomez Cano, David Vazquez Bermudez +1
One of the challenges in robotics is to enable robotic units with the reasoning capability that would be robust enough to execute complex tasks in dynamic environments. Recent adva…