Inverse scattering theory investigates the reconstruction of unknown objects or media by analysing how incident waves are scattered. At its core, the theory combines partial differential equations, ...
Inverse problems arise when one seeks to recover unknown parameters or functions from indirect, noisy observations via a forward model. The Bayesian framework casts this recovery as the updating of a ...
Abstract: Linear optical sampling (LOS) technique revolutionizes the acquisition of full-field information from the ultrafast optical signals, based on small bandwidth optoelectronic devices. In ...
Abstract: Flow matching is a recent state-of-the-art framework for generative modeling based on ordinary differential equations (ODEs). While closely related to diffusion models, it provides a more ...
This repository implements a post-training confidence estimation pipeline for a radiology report generation model. Given a chest X-ray, MedGemma generates a free-text radiology report. Confidence ...
Enhancing large language model (LLM) reasoning through Monte Carlo Tree Search (MCTS). Rather than sampling a single chain of thought, this framework builds an explicit search tree over partial ...
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