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Deep Kernels

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If you have a question about this talk, please contact Elre Oldewage.

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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