Yoshua Bengio’s talk in London

Yesterday I attended a talk organized by the London Machine Learning meetup group, where Yoshua Bengio was the invited speaker. Not surprisingly, there were about 200 people attending.

Yoshua reinforced the idea that a lot of the success of learning algorithms for AI tasks comes from incorporating meaningful priors. These should be general enough to hold true in a wide range of applications, but also specific enough to vastly reduce the amount of training data needed to achieve good generalization. This reminded me of a previous post I wrote in this blog, almost 5 years ago!

In the meanwhile, deep learning became main-stream, and Yohua slides high-lighted several theoretical progresses that were made, e.g:

  1. Expressiveness of deep networks with piecewise linear activation functions: exponential advantage for depth (Montufar et al NIPS 2014)
  2. Theoretical and empirical evidence against bad local minima (Dauphin et al NIPS 2014)
  3. Manifold and probabilistic interpretations of auto-encoders:

Enjoy reading!

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