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Jun 18, 2024 · The major contribution of the presented work is a principled approach on establishing Gaussian processes on the product of directional manifolds. We propose ...
Jun 17, 2024 · Prevailing uncertainty in geopolitical, economic and regulatory environments demands a more dynamic approach to default modelling. ... Gaussian Process Classifier ...
5 days ago · In this paper, we propose a stochastic variational inference approach for the LV-MOGP that allows mini-batches for both inputs and outputs, making computational ...
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 29, 2024 · Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective. This classical approach known as Type-II ...
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 ( ...
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 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 ...
Jun 19, 2024 · Bayesian Optimization (BO) is a standard approach for optimizing black box functions using a surrogate model of the objective [Močkus, 1975 , Snoek et al., 2012 ...
Jun 28, 2024 · In this paper, we present a kernel-based approach that extends continuous exponential kernels to handle mixed-categorical variables. The proposed kernel leads ...