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A Probabilistic Framework for Deep Learning
Ankit Patel · Minh Nguyen · Richard Baraniuk

Wed Dec 07 09:00 AM -- 12:30 PM (PST) @ Area 5+6+7+8 #57 #None

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.

Author Information

Ankit Patel (Baylor College of Medicine and Rice University)
Minh Nguyen (Rice University)
Richard Baraniuk (Rice University)

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