Presented at Fields Institute
Barak Pearlmutter, Hamilton Institute, Maynooth
Near-Criticality and Pathology in the Central Nervous System
Abstract
Suchi Saria, Johns Hopkins University
Individualizing Healthcare with Machine Learning (Video)
Abstract
Peter Grünwald, Leiden University
Learning the learning rate: how to repair Bayes when the model is wrong (Video)
Abstract
Emma Brunskill, Stanford University
Reinforcement Learning When Experience is Expensive (Video)
Abstract
Josh Tenenbaum, Massachusetts Institute of Technology
Building machines that learn and think like humans (Video)
Abstract
Chris Maddison, University of Oxford
Relaxed Gradient Estimators (Video)
Abstract
Yair Weiss, Hebrew University
Learning the Statistics of Full Images (Video)
Abstract
Christopher Manning, Stanford University
Towards a better model for neural network reasoning (Video)
Abstract
Vlad Mnih, DeepMind
Efficient Multi-Task Deep Reinforcement Learning (Video)
Abstract
Stephen Friend, Sage Bionetworks
Exploring fundamental unknowns that prevent us from using our devices to navigate between disease and health (Video)
Abstract
Ilya Sutskever, OpenAI
Meta Learning and Self Play (Video)
Abstract
Yee Whye Teh, University of Oxford
DisTraL: Distill and Transfer for Deep Multitask Reinforcement Learning (Video)
Abstract
Guang Wei Yu (Layer 6), Mohammad Islam (Wattpad), Putra anggala (Shopify), and Javier Moreno (Rubikloud)
Recommendation Systems (Video)
Abstract
Jennifer Listgarten, Microsoft Research
From Genetics to CRISPR Gene Editing with Machine Learning (Video)
Abstract
Chris Williams, Univeristy of Edinburgh
Artificial Intelligence for Data Analytics (Video)
Abstract
Jon Shlens, Google Brain
Learning representations of the visual world
Abstract
Geoffrey Hinton, University of Toronto
What is wrong with convolutional neural nets? (Video)
Abstract