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• W2952372065 abstract "The composition of multiple Gaussian Processes as a Deep Gaussian Process (DGP) enables a deep probabilistic nonparametric approach to flexibly tackle complex machine learning problems with sound quantification of uncertainty. Existing inference approaches for DGP models have limited scalability and are notoriously cumbersome to construct. In this work, we introduce a novel formulation of DGPs based on random feature expansions that we train using stochastic variational inference. This yields a practical learning framework which significantly advances the state-of-the-art in inference for DGPs, and enables accurate quantification of uncertainty. We extensively showcase the scalability and performance of our proposal on several datasets with up to 8 million observations, and various DGP architectures with up to 30 hidden layers." @default.
• W2952372065 created "2019-06-27" @default.
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• W2952372065 date "2016-10-14" @default.
• W2952372065 modified "2023-09-27" @default.
• W2952372065 title "Random Feature Expansions for Deep Gaussian Processes" @default.
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• W2952372065 hasPublicationYear "2016" @default.
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