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We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a novel framework for learning deep kernel Gaussian processes (GPs) by interpolating between meta-learning and conventional deep kernel learning.
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Scalable one-pass optimisation of high-dimensional weight-update hyperparameters by implicit differentiation
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