HTC Seminar Series #17- From Deep Learning of Distangled Representation to Higher Level Cognition
Today's HTC seminar is a great presentation by Yoshua Bengio at Microsoft Research in 2018. The first half has a lot of relevance to our recent 'manipulate the embedded representation of a GAN' discussions in some recent blog posts. The second half talks about how to move deep learning up the cognitive food chain.
In 2018, Yoshua Bengio ranked as the computer scientist with the most new citations worldwide, thanks to his many high-impact contributions. In 2019, he received the ACM A.M. Turing Award, “the Nobel Prize of Computing”, jointly with Geoffrey Hinton and Yann LeCun for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing.
The talk starts by reviewing earlier work on the notion of learning disentangled representations and deep generative models and propose research directions towards learning of high-level abstractions. This follows the ambitious objective of disentangling the underlying causal factors explaining the observed data.
Yoshua argues that in order to efficiently capture these, a learning agent can acquire information by acting in the world, moving our research from traditional deep generative models of given datasets to that of autonomous learning or unsupervised reinforcement learning.