2 citations · 2 across the 11 of their papers we have counts for
11 papers
Self-Rewarding Sequential Monte Carlo for Masked Diffusion Language Models
Ziwei Luo, Ziqi Jin, Lei Wang +2
This work presents self-rewarding sequential Monte Carlo (SMC), an inference-time scaling algorithm enabling effective sampling of masked diffusion language models (MDLMs). Our alg…
Efficient Image Restoration with State-Dependent Forward Diffusion
Ziwei Luo, Fredrik K. Gustafsson, Jens Sjölund +1
This paper proposes to perform image restoration through a state-dependent mean-reverting forward diffusion (FoD) process. In contrast to traditional diffusion-based approaches tha…
Online learning in motion modeling for intra-interventional image sequences
Niklas Gunnarsson, Jens Sjölund, Peter Kimstrand +1
Image monitoring and guidance during medical examinations can aid both diagnosis and treatment. However, the sampling frequency is often too low, which creates a need to estimate t…
Taming Diffusion Models for Image Restoration: A Review
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
Diffusion models have achieved remarkable progress in generative modelling, particularly in enhancing image quality to conform to human preferences. Recently, these models have als…
Conditional sampling within generative diffusion models
Zheng Zhao, Ziwei Luo, Jens Sjölund +1
Generative diffusions are a powerful class of Monte Carlo samplers that leverage bridging Markov processes to approximate complex, high-dimensional distributions, such as those fou…
Hallucination Detection in LLMs: Fast and Memory-Efficient Fine-Tuned Models
Gabriel Y. Arteaga, Thomas B. Schön, Nicolas Pielawski
Uncertainty estimation is a necessary component when implementing AI in high-risk settings, such as autonomous cars, medicine, or insurances. Large Language Models (LLMs) have seen…
Accounts of using the Tustin-Net architecture on a rotary inverted pendulum
Stijn van Esch, Fabio Bonassi, Thomas B. Schön
In this report we investigate the use of the Tustin neural network architecture (Tustin-Net) for the identification of a physical rotary inverse pendulum. This physics-based archit…
Conditioning diffusion models by explicit forward-backward bridging
Adrien Corenflos, Zheng Zhao, Simo Särkkä +2
Given an unconditional diffusion model targeting a joint model , using it to perform conditional simulation is still largely an open question and is typicall…
Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models
Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao +2
Though diffusion models have been successfully applied to various image restoration (IR) tasks, their performance is sensitive to the choice of training datasets. Typically, diffus…
Entropy-regularized Diffusion Policy with Q-Ensembles for Offline Reinforcement Learning
Ruoqi Zhang, Ziwei Luo, Jens Sjölund +2
This paper presents advanced techniques of training diffusion policies for offline reinforcement learning (RL). At the core is a mean-reverting stochastic differential equation (SD…
On Feynman--Kac training of partial Bayesian neural networks
Zheng Zhao, Sebastian Mair, Thomas B. Schön +1
Recently, partial Bayesian neural networks (pBNNs), which only consider a subset of the parameters to be stochastic, were shown to perform competitively with full Bayesian neural n…