Having heard Geoffrey Hinton’s somewhat dismissive account of the contribution by physicists to machine learning in his online MOOC, it was interesting to listen to one of those physicists, Naftali Tishby, here at PI:
I was familiar with the general concept of over-fitting, but I hadn’t realized you could talk about it quantitatively by looking at the mutual information between the output of a network and all the information in the training data that isn’t the target label.
One often hears the refrain that a lot of ML techniques were known for decades but only became useful when big computational power and huge datasets arrived relatively recently. The unreasonable effectiveness of data is often described as a surprise, but Tishby claims that (part of?) this was predicted by the physicists based on large-N limits of statistical mechanics models, but that this was ignored by the computer scientists. I don’t know near enough about this topic to assess.
He clearly has a chip on his shoulder — which naturally makes me like him. His “information bottleneck” paper with Pereira and Bialek was posted to the arXiv in 2000 and apparently rejected by the major CS conferences, but has since accumulated fourteen hundred citations.… [continue reading]