Results 11 to 20 of about 848,965 (236)
Speeding up quantum dissipative dynamics of open systems with kernel methods
New Journal of Physics, 2021 The future forecasting ability of machine learning (ML) makes ML a promising tool for predicting long-time quantum dissipative dynamics of open systems. In this article, we employ nonparametric ML algorithm (kernel ridge regression as a representative of
Arif Ullah, Pavlo O. Dral
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Physics-aware nonparametric regression models for Earth data analysis
Environmental Research Letters, 2022 Process understanding and modeling is at the core of scientific reasoning. Principled parametric and mechanistic modeling dominated science and engineering until the recent emergence of machine learning (ML).
Jordi Cortés-Andrés +9 more
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IEEE Access, 2023 One of the powerful techniques in data modeling is accounting for features that are available at the training stage, but are not available when the trained model is used to classify or predict test data — Learning Using Privileged Information ...
Andrey Lange +2 more
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Journal of Telecommunications and Information Technology, 2022 In recent years, kernel methods have provided an important alternative solution, as they offer a simple way of expanding linear algorithms to cover the non-linear mode as well.
Rachid Fateh, Anouar Darif, Said Safi
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A Novel Boolean Kernels Family for Categorical Data
Entropy, 2018 Kernel based classifiers, such as SVM, are considered state-of-the-art algorithms and are widely used on many classification tasks. However, this kind of methods are hardly interpretable and for this reason they are often considered as black-box models ...
Mirko Polato +2 more
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Heat kernel methods for Lifshitz theories [PDF]
, 2017 We study the one-loop covariant effective action of Lifshitz theories using the heat kernel technique. The characteristic feature of Lifshitz theories is an anisotropic scaling between space and time.
Barvinsky, Andrei O. +5 more
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Estimating Road Segments Using Kernelized Averaging of GPS Trajectories
Applied Sciences, 2019 A method called iTEKA, which stands for iterative time elastic kernel averaging, was successfully used for averaging time series. In this paper, we adapt it to GPS trajectories. The key contribution is a denoising procedure that includes an over-sampling
Pierre-François Marteau
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Applied Sciences, 2021 Cross-frequency interactions, a form of oscillatory neural activity, are thought to play an essential role in the integration of distributed information in the brain.
Iván De La Pava Panche +4 more
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BMC Bioinformatics, 2023 Background In this paper, we are interested in interactions between a high-dimensional -omics dataset and clinical covariates. The goal is to evaluate the relationship between a phenotype of interest and a high-dimensional omics pathway, where the effect
Charlie M. Carpenter +4 more
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FEW-GROUP CROSS SECTIONS LIBRARY BY ACTIVE LEARNING WITH SPLINE KERNELS [PDF]
EPJ Web of Conferences, 2021 This work deals with the representation of homogenized few-groups cross sections libraries by machine learning. A Reproducing Kernel Hilbert Space (RKHS) is used for different Pool Active Learning strategies to obtain an optimal support.
Szames E. +3 more
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