Latent diffusion models for generative precipitation nowcasting with accurate uncertainty quantification [PDF]
arXiv.org, 2023 Diffusion models have been widely adopted in image generation, producing higher-quality and more diverse samples than generative adversarial networks (GANs).
J. Leinonen +4 more
semanticscholar +1 more source
Bayes' Rays: Uncertainty Quantification for Neural Radiance Fields [PDF]
Computer Vision and Pattern Recognition, 2023 Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertain-ties.
Lily Goli +4 more
semanticscholar +1 more source
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles [PDF]
npj Computational Materials, 2023 Neural networks (NNs) often assign high confidence to their predictions, even for points far out of distribution, making uncertainty quantification (UQ) a challenge.
Aik Rui Tan +4 more
semanticscholar +1 more source
The Gaussian Process Modeling Module in UQLab [PDF]
Journal of Soft Computing in Civil Engineering, 2018 We introduce the Gaussian process (GP) modeling module developed within the UQLab software framework. The novel design of the GP-module aims at providing seamless integration of GP modeling into any uncertainty quantification workflow, as well as a ...
Christos Lataniotis +2 more
doaj +1 more source
Geometry of martensite needles in shape memory alloys
Comptes Rendus. Mathématique, 2021 We study the geometry of needle-shaped domains in shape-memory alloys. Needle-shaped domains are ubiquitously found in martensites around macroscopic interfaces between regions which are laminated in different directions, or close to macroscopic ...
Conti, Sergio +4 more
doaj +1 more source
Quantum-Inspired Uncertainty Quantification
Frontiers in Computer Science, 2022 Reasonable quantification of uncertainty is a major issue of cognitive infocommunications, and logic is a backbone for successful communication. Here, an axiomatic approach to quantum logic, which highlights similarity to and differences to classical ...
Günther Wirsching
doaj +1 more source
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods [PDF]
arXiv.org, 2023 The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption
Ling Huang +3 more
semanticscholar +1 more source
Contaminant source localization via Bayesian global optimization [PDF]
Hydrology and Earth System Sciences, 2019 Contaminant source localization problems require efficient and robust methods that can account for geological heterogeneities and accommodate relatively small data sets of noisy observations.
G. Pirot +7 more
doaj +1 more source
Uncertainty quantification and propagation with probability boxes
Nuclear Engineering and Technology, 2021 In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nuclear power plant design have become a trending topic in the nuclear field.
L. Duran-Vinuesa, D. Cuervo
doaj +1 more source
Federated Conformal Predictors for Distributed Uncertainty Quantification [PDF]
International Conference on Machine Learning, 2023 Conformal prediction is emerging as a popular paradigm for providing rigorous uncertainty quantification in machine learning since it can be easily applied as a post-processing step to already trained models. In this paper, we extend conformal prediction
Charles Lu +4 more
semanticscholar +1 more source