Plug-and-Play (PnP) algorithms are a class of iterative methods for inverse imaging. Within an optimization algorithm, they combine a flexible fidelity term, encoding the forward operator, and a pretrained image denoiser, in order to deal with more severe corruptions such as blurring or downsampling when reconstructing...
Zi-Qi Qin, Hong-Wei Tan, A. Biguri et al.· 0 citations
Chain-of-thought reasoning provides a structured computation between a model's input and final answer. Yet it is often evaluated through endpoint accuracy, which ignores the path taken to reach that answer. An emerging line of work addresses this limitation using entropy profiles, which track how uncertainty evolves ov...
M. Catala, Haitz Sáez de Ocáriz Borde, D. Murari et al.· 0 citations
This work systematically investigates how evaluation-reference choices affect model performance and ranking in both pathology classification and image quality assessment (IQA), and shows that for supervised image classifiers, changing the label source leads to substantial differences not only in performance estimates b...
Panagiotis Fytas, Ian Selby, C. Karner et al.· arXiv.org· 0 citations
This work constructs and analyzes a class of 1-Lipschitz neural networks on Hadamard manifolds, and shows improved results from the nonexpansive denoiser over static, data-only, and Log-Euclidean denoising baselines, and empirically test its convergence properties.
D. Murari, Marta Ghirardelli, Ben Adcock et al.· arXiv.org· 0 citations
This work presents a modular framework for discovering piecewise-smooth dynamical systems by first estimating switching hyperplanes from data and then learning smooth dynamics within each region using geometry-constrained neural networks.
D. Murari, Erik Jansson, Chris Budd Obe et al.· 0 citations
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