From the 1 of 4 linked papers with an AI index.
4 papers
Learning Latent Energy-Based Models via Interacting Particle Langevin Dynamics
Joanna Marks, Tim Y. J. Wang, O. Deniz Akyildiz
The paper proposes a continuous‑time framework using interacting particle Langevin dynamics to learn latent variable models with energy‑based priors, provides a discretized algorit…
Consistency Regularised Gradient Flows for Inverse Problems
Alessio Spagnoletti, Tim Y. J. Wang, Marcelo Pereyra +1
Vision-Language Latent Diffusion Models (LDMs) (Rombach et al., 2022) provide powerful generative priors for inverse problems. However, existing LDM-based inverse solvers typically…
Training Latent Diffusion Models with Interacting Particle Algorithms
Tim Y. J. Wang, Juan Kuntz, O. Deniz Akyildiz
We introduce a novel particle-based algorithm for end-to-end training of latent diffusion models. We reformulate the training task as minimizing a free energy functional and obtain…
A Gradient Flow Approach to Solving Inverse Problems with Latent Diffusion Models
Tim Y. J. Wang, O. Deniz Akyildiz
Solving ill-posed inverse problems requires powerful and flexible priors. We propose leveraging pretrained latent diffusion models for this task through a new training-free approac…