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7 May 2019 Extremely deep Bayesian learning with Gromov's method
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Abstract
We have invented two new Bayesian deep learning algorithms using stochastic particle flow to compute Bayes’ rule. These learning algorithms have a continuum of layers, in contrast with 10 to 100 discrete layers in standard deep learning neural nets. We compute Bayes’ rule for learning using a stochastic particle flow designed with Gromov’s method. Both deep learning and standard particle filters suffer from the curse of dimensionality, and we mitigate this problem by using stochastic particle flow to compute Bayes’ rule. The intuitive explanation for the dramatic reduction in computational complexity is that stochastic particle flow adaptively moves particles to the correct region of d dimensional space to represent the multivariate probability density of the state vector conditioned on the data. There is nothing analogous to this in standard neural nets (deep or shallow), where the geometry of the network is fixed.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Fred Daum, Jim Huang, and Arjang Noushin "Extremely deep Bayesian learning with Gromov's method", Proc. SPIE 11018, Signal Processing, Sensor/Information Fusion, and Target Recognition XXVIII, 110180I (7 May 2019); https://doi.org/10.1117/12.2517980
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