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Deep Kernels

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Deep kernel learning methods try to combine the expressive power of neural networks with the uncertainty representation of Gaussian processes. This is achieved by learning a feature extractor to transform the input data before using a Gaussian process model. In this talk, we will describe what deep kernel learning is in depth, before discussing recent advances and insights.

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This talk is part of the Machine Learning Reading Group @ CUED series.

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