Neural Operator Variational Inference Based on Regularized Stein Discrepancy for Deep Gaussian Processes

Deep Gaussian process (DGP) models offer a powerful nonparametric approach for Bayesian inference, but exact inference is typically intractable, motivating the use of various approximations. However, existing approaches, such as mean-field Gaussian assumptions, limit the expressiveness and efficacy...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems 2024-08, Vol.PP, p.1-15
Hauptverfasser:	Xu, Jian, Du, Shian, Yang, Junmei, Ma, Qianli, Zeng, Delu
Format:	Artikel
Sprache:	eng
Schlagworte:	Computational modeling Convergence Deep Gaussian processes (DGPs) Gaussian processes Generators Global Positioning System neural network generator operator variational inference (VI) Optimization Training
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