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Deep Kernel Machines via the Kernel Reparametrization Trick
Jovana Mitrovic, Dino Sejdinovic, Yee Whye Teh
Feb 17, 2017 (modified: Feb 17, 2017)ICLR 2017 workshop submissionreaders: everyone
Abstract:While deep neural networks have achieved state-of-the-art performance on many tasks across varied domains, they still remain black boxes whose inner workings are hard to interpret and understand. In this paper, we develop a novel method for efficiently capturing the behaviour of deep neural networks using kernels. In particular, we construct a hierarchy of increasingly complex kernels that encode individual hidden layers of the network. Furthermore, we discuss how our framework motivates a novel supervised weight initialization method that discovers highly discriminative features already at initialization.
TL;DR:Encoding deep neural networks using a hierarchy of increasingly complex kernels. This motivates a novel supervised weight initialization method that discovers highly discriminative features already at initialization.
Keywords:Theory, Deep learning
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