Research Interests
My long-term goal is to understand the mechanisms giving rise to intelligence; understanding the underlying principles would deliver artificial intelligence, and I believe that learning algorithms are essential in this quest.

Since 1986 I have worked on neural networks, in particular on deep learning in this century. What fascinates me is how an intelligent agent, animal, human or machine, can figure out how their environment works. Of course this can be used to make good decisions, but I feel like at the heart is the notion of understanding, and the crucial question is how to learn to understand.

In the past I worked on learning of deep representations (either supervised or unsupervised), capturing sequential dependencies with recurrent networks and other autoregressive models, understanding credit assignment (including the quest for biologically plausible analogues of backprop, as well as end-to-end learning of complex modular information processing assemblies), meta-learning (or learning to learn), attention mechanisms, deep generative models, curriculum learning, variations of stochastic gradient descent and why SGD works for neural nets, convolutional architectures, natural language processing (especially with word embeddings, language models and machine translation), understanding why deep learning works so well and what its current limitations are.