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We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...
Correlated errors of experimental data are a common but often neglected problem in physical sciences. Various tools are provided here for thorough propagation of uncertainties in cases of correlated errors. Discussed are techniques especially applicable to ...
Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...
There is a paradox in the relationship between bedload transport rates and flow variables: laboratory and field studies have reported on how bedload transport rates depend on flow variables through a power law, but none of the empirical laws fitted to the ...
Objective: To characterize ambulatory knee moments with respect to medial knee osteoarthritis (OA) severity comprehensively and to assess the possibility of developing a severity index combining knee moment parameters. Methods: Nine parameters (peak amplit ...
Unsupervised Domain Adaptation Regression (DAR) aims to bridge the domain gap between a labeled source dataset and an unlabelled target dataset for regression problems. Recent works mostly focus on learning a deep feature encoder by minimizing the discrepa ...
In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compare ...
The Institute of Electrical and Electronics Engineers, Inc2023
Climate-change-induced extreme weather events increase heat-related mortality and health risks for urbanites, which may also affect urbanites’ expressed happiness (EH) and well-being. However, the links among EH, climate, and socioeconomic factors remain u ...
This article explores how the logic underlying modal practices tends to modulate the population’s responsiveness to improvements in global transport supply. Based on a quantitative survey conducted in 2018–2019 among the working population of the cantons o ...
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability impr ...