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• W2949655905 abstract "We propose a novel deep learning paradigm of differential flows that learn a stochastic differential equation transformations of inputs prior to a standard classification or regression function. The key property of differential Gaussian processes is the warping of inputs through infinitely deep, but infinitesimal, differential fields, that generalise discrete layers into a dynamical system. We demonstrate state-of-the-art results that exceed the performance of deep Gaussian processes and neural networks" @default.
• W2949655905 created "2019-06-27" @default.
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• W2949655905 date "2018-10-09" @default.
• W2949655905 modified "2023-09-27" @default.
• W2949655905 title "Deep learning with differential Gaussian process flows" @default.
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• W2949655905 hasPublicationYear "2018" @default.
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