8 papers
Efficient Flow Matching for Sparse-View CT Reconstruction
Jiayang Shi, Lincen Yang, Zhong Li +3
Generative models, particularly Diffusion Models (DM), have shown strong potential for Computed Tomography (CT) reconstruction serving as expressive priors for solving ill-posed in…
Deep Reinforcement Learning for Optimizing Angle Selection and Dose Allocation in CT Reconstruction
Tianyuan Wang, Daniël M. Pelt, Felix Lucka +2
Traditional X-ray computed tomography (CT) scanning strategies typically select projection angles uniformly and allocate dose equally. In practice, however, CT scans often need to…
Noise2Ghost: Self-supervised deep convolutional reconstruction for ghost imaging
Mathieu Manni, Dmitry Karpov, K. Joost Batenburg +2
We present a new self-supervised deep-learning-based Ghost Imaging (GI) reconstruction method, which provides unparalleled reconstruction quality for noisy acquisitions among unsup…
DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
Jiayang Shi, Daniel M. Pelt, K. Joost Batenburg
Diffusion models have recently emerged as powerful priors for solving inverse problems. While computed tomography (CT) is theoretically a linear inverse problem, it poses many prac…
Agentic Large Language Models, a survey
Aske Plaat, Max van Duijn, Niki van Stein +3
Background: There is great interest in agentic LLMs, large language models that act as agents. Objectives: We review the growing body of work in this area and provide a research ag…
Analysis of Bluffing by DQN and CFR in Leduc Hold'em Poker
Tarik Zaciragic, Aske Plaat, K. Joost Batenburg
In the game of poker, being unpredictable, or bluffing, is an essential skill. When humans play poker, they bluff. However, most works on computer-poker focus on performance metric…