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Jun 17, 2024 · Prevailing uncertainty in geopolitical, economic and regulatory environments demands a more dynamic approach to default modelling. ... Gaussian Process Classifier ...
Jun 24, 2024 · This approach facilitates the model in better understanding and representing code and query. Meanwhile, we also train the model using metric learning with the ...
Jun 4, 2024 · We introduce a scalable approach to Gaussian process inference that combines spatio-temporal filtering with natural gradient variational inference, ...
Jun 21, 2024 · Che, Wang, Lin, and Ni (2022) used VAE for data augmentation of samples and metric-based meta-learning approach for fault diagnosis. Ge, Song, Li, and Zhang ( ...
Jun 7, 2024 · We introduce a new framework for approximate Bayesian uncertainty quantification in neural operators using function- valued Gaussian processes. Our approach can ...
Deep Kernel learning for reaction outcome prediction and optimization
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Jun 14, 2024 · It is found that the DKL approach provides better predictive uncertainty estimations compared to standard GPs. ... Gaussian processes for machine learning.
Jun 13, 2024 · This paper introduces a novel deep time series clustering approach integrating VAE with metric learning. ... Gaussian framework, it uses Gaussian Mixture Model ( ...
23 hours ago · Our proposed multifidelity cross-validation (MFCV) approach develops an adaptive approach to reduce the LOO-CV error at the target (highest) fidelity, by ...
24 hours ago · The properties of our approach are illustrated in terms of new parametric classes of matrix-valued kernels for product spaces of a hypersphere crossed with a ...
Jun 12, 2024 · Our approach presents the first general-purpose collaborative BO framework that is compatible with any Gaussian process kernel and most of the known acquisition ...