Wednesday, September 27, 2017

Videos: Montreal AI Symposium






So the Montreal AI Symposium is currently happening in Montreal. Here are the videos of the morning and afternoon sessions after the program (and congrats to the organizers Hugo Larochelle, Joëlle Pineau, Adam Trischler, Nicolas Chapados, Guillaume Chicoisne for put these presentations online through the streaming):


Keynote — Artificial Intelligence Goes All-In: Computers Playing Poker
Michael Bowling, University of Alberta and DeepMind
9.50 – 10.10 Contributed talk — A Distributional Perspective on Reinforcement Learning
Marc G. Bellemare, Google Brain
10.10 – 10.30 Contributed talk — Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, McGill University, OpenAI; Yi Wu, UC Berkeley; Aviv Tamar, UC Berkeley; Jean Harb, McGill University, OpenAI; Pieter Abbeel, UC Berkeley, Openai; Igor Mordatch, OpenAI
11.00 – 11.20
Contributed talk — Team Sports Modelling
Norm Ferns, SPORTLOGiQ; Mehrsan Javan, SPORTLOGiQ
11.20 – 11.40
Contributed talk — FigureQA: An annotated figure dataset for visual reasoning
Samira Ebrahimi Kahou, Microsoft; Adam Atkinson, Microsoft; Vincent Michalski, University of Montreal; Akos Kadar, Microsoft; Adam Trischler, Microsoft; Yoshua Bengio, University of Montreal
11.40 – 12.00
Contributed talk — FiLM: Visual Reasoning with a General Conditioning Layer
Ethan Perez, MILA and Rice University; Harm de Vries, MILA; Florian Strub, Université Lille; Vincent Dumoulin, MILA; Aaron Courville, MILA and CIFAR
13.30 – 14.10
Keynote — Deep Learning for Self-Driving Cars
Raquel Urtasun, University of Toronto and Uber

14.10 – 14.30
Contributed Talk — Deep 6-DOF Tracking
Mathieu Garon, Université Laval; Jean-François Lalonde, Université Laval

14.30 – 14.50
Contributed Talk — Deep Learning for Character Animation
Daniel Holden, Ubisoft Montreal
15.20 – 15.40
Contributed Talk — Assisting combinatorial chemistry in the search of highly bioactive peptides
Prudencio Tossou, Université Laval; Mario Marchand, Université Laval; François Laviolette, Université Laval

15.40 – 16.00
Contributed Talk — Saving Newborn Lives at Birth through Machine Learning
Charles Onu, Ubenwa Intelligence Solutions Inc; Doina Precup, McGill University

16.00 – 16.20
Contributed Talk — Meticulous Transparency — A Necessary Practice for Ethical AI
Abhishek Gupta ; Dr. David Benrimoh

17.00 – 20.00

Poster Session + Happy Hour with Sponsors

Morning session



Afternoon session


h/t Hugo


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1 comment:

Sean O'Connor said...

I mentioned over a the Numenta forum that one perspective on deep neural networks views them as pattern based fuzzy logic.
https://discourse.numenta.org/t/artificial-life-concept/2308/11
In particular in higher dimension the dot product weighting function used in neural nets acts as a selective filter. Or you can say the dot product weighting produces a low magnitude output for most any random input and only a small number of select input vectors will produce a high magnitude output.
https://www.cs.princeton.edu/courses/archive/fall14/cos521/lecnotes/lec11.pdf

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