Open AccessProceedings Article
Kernel Methods for Deep Learning
Youngmin Cho,Lawrence K. Saul +1 more
- 07 Dec 2009
- Vol. 22, pp 342-350
TL;DR: A new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets are introduced that can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that the authors call multilayers kernel machines (MKMs).
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Abstract: We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. These kernel functions can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that we call multilayer kernel machines (MKMs). We evaluate SVMs and MKMs with these kernel functions on problems designed to illustrate the advantages of deep architectures. On several problems, we obtain better results than previous, leading benchmarks from both SVMs with Gaussian kernels as well as deep belief nets.
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Citations
Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising 1 criterion
P. Vincent
- 01 Jan 2010
TL;DR: This work clearly establishes the value of using a denoising criterion as a tractable unsupervised objective to guide the learning of useful higher level representations.
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- 01 Jan 2018
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Deep Learning: Methods and Applications
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TL;DR: This talk will introduce this formalism and give a number of results on the Neural Tangent Kernel and explain how they give us insight into the dynamics of neural networks during training and into their generalization features.
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Gradient-based learning applied to document recognition
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