Post-doc Openings at Mila: Yoshua Bengio is leading a group of Mila professors working on machine learning for drug discovery (Jian Tang, Doina Precup, Pierre-Luc Bacon, Sarath Chandar, Amine Emad, Guy Wolf, Mathieu Blanchette, and myself) and looking for a couple of postdocs. One of them could play more of a project management role, coordinating projects and supervising junior students. Please fill the form here and write to Yoshua.
See this 2021 talk on ML for searching in the space of drugs and biological or chemical experiments.
Job Openings at Mila: I am looking for a project manager on the drug discovery projects.
My current fundamental research vision was summarized by my Posner lecture at NeurIPS 2019: From System 1 Deep Learning to System 2 Deep Learning, December 11th, 2019. Video with synchronized slides here.
Tutorial at IJCAI’2018 on Deep Learning for AI, July 13th, 2018.
Call for an International Ban on the Weaponization of Artificial Intelligence; AI Researchers ask the Canadian government to act at the UN in an open letter which you can sign too.
Video of my keynote at the first Cognitive and Computational Neuroscience conference at Columbia University on September 8, 2017.
Montreal Deep Learning Summit, 10-11 Oct 2017, with Geoff Hinton, Yann LeCun and Yoshua Bengio
2017 Montreal Deep Learning Summer School and Reinforcement Learning Summer School and its video lectures
Participation at the Beneficial AI Conference with video of my presentation.
Introductory articles in Scientific American about Deep Learning and AI.
More research highlights and selected recent papers
Nature paper on Deep Learning by Yann LeCun, Yoshua Bengio and Geoff Hinton (pdf)
NIPS’2015 Deep Learning Tutorial and the block of slides for the Vision part
NIPS’2014 Deep Learning and Representation Learning Workshop
Deep Learning – an MIT Press book now for sale here
ICLR: the International Conference on Learning Representations
2012 Review paper (published 2013 in PAMI) on Representation Learning
2012 Review paper on Practical recommendations for gradient-based training of deep architectures