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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