A Probabilistic Framework for Deep Learning
Author
Patel, Ankit B.; Nguyen, Tan; Baraniuk, Richard G.
Date
2016Abstract
We develop a probabilistic framework for deep learning based on the Deep Rendering Mixture Model (DRMM), a new generative probabilistic model that explicitly capture variations in data due to latent task nuisance variables. We demonstrate that max-sum inference in the DRMM yields an algorithm that exactly reproduces the operations in deep convolutional neural networks (DCNs), providing a first principles derivation. Our framework provides new insights into the successes and shortcomings of DCNs as well as a principled route to their improvement. DRMM training via the Expectation-Maximization (EM) algorithm is a powerful alternative to DCN back-propagation, and initial training results are promising. Classification based on the DRMM and other variants outperforms DCNs in supervised digit classification, training 2-3x faster while achieving similar accuracy. Moreover, the DRMM is applicable to semi-supervised and unsupervised learning tasks, achieving results that are state-of-the-art in several categories on the MNIST benchmark and comparable to state of the art on the CIFAR10 benchmark.
Description
NEWS COVERAGE: A news release based on this journal publication is available online: http://news.rice.edu/2016/12/16/rice-baylor-team-sets-new-mark-for-deep-learning/
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Type
Journal article
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Citable link to this page
https://hdl.handle.net/1911/93745Link to related resources
http://papers.nips.cc/paper/6231-a-probabilistic-framework-for-deep-learningMetadata
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