History of Bayesian Neural Networks

Written by: Stephen Hsu

Primary Source: Information Processing

This talk gives the history of neural networks in the framework of Bayesian inference. Deep learning is (so far) quite empirical in nature: things work, but we lack a good theoretical framework for understanding why or even how. The Bayesian approach offers some progress in these directions, and also toward quantifying prediction uncertainty.

I was sad to learn from this talk that David Mackay passed last year, from cancer. I recommended his book Information theory, inference and learning algorithms back in 2007.

Yarin Gal’s dissertation Uncertainty in Deep Learning, mentioned in the talk.

I suppose I can thank my Caltech education for a quasi-subconscious understanding of neural nets despite never having worked on them. They were in the air when I was on campus, due to the presence of John Hopfield (he co-founded the Computation and Neural Systems PhD program at Caltech in 1986). See also Hopfield on physics and biology.

Amusingly, I discovered this talk via deep learning: YouTube’s recommendation engine, powered by deep neural nets, suggested it to me this Saturday afternoon :-)

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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