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Deep Gaussian Processes for Regression using Approximate Expectation Propagation

, , , , и . (2016)cite arxiv:1602.04133.

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Richard -E Moes

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Meta-Learning Probabilistic Inference for Prediction., , , , и . ICLR (Poster), OpenReview.net, (2019)On Sparse Variational Methods and the Kullback-Leibler Divergence between Stochastic Processes., , , и . AISTATS, том 51 из JMLR Workshop and Conference Proceedings, стр. 231-239. JMLR.org, (2016)Convolutional conditional neural processes for local climate downscaling, , , и . Geoscientific Model Development, 15 (1): 251-268 (2022)Optimising Distributions with Natural Gradient Surrogates., и . CoRR, (2023)Probabilistic Amplitude Demodulation., и . ICA, том 4666 из Lecture Notes in Computer Science, стр. 544-551. Springer, (2007)Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes., , , , , и . NeurIPS, (2020)Tree-structured Gaussian Process Approximations., и . NIPS, стр. 2213-2221. (2014)HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals., , , , , и . AABI, том 118 из Proceedings of Machine Learning Research, стр. 1-8. PMLR, (2019)Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model., , , , , и . NeurIPS, стр. 14791-14802. (2019)Learning Stationary Time Series using Gaussian Processes with Nonparametric Kernels., , и . NIPS, стр. 3501-3509. (2015)