Research
Publications
CVPR 2019
DeepFlux for Skeletons in the Wild
By Yukang Wang, Yongchao Xu, Stavros Tsogkas, Xiang Bai, Sven Dickinson, Kaleem Siddiqi
Scene Categorization from Contours: Medial Axis Based Salience Measures
By Morteza Rezanejad, Gabriel Downs, John Wilder, Dirk B. Walther, Allan Jepson, Sven Dickinson, Kaleem Siddiqi
ICLR 2019
Explaining Image Classifiers by Counterfactual Generation
By Chun-Hao Chang, Elliot Creager, Anna Goldenberg, David Duvenaud
Visual Reasoning by Progressive Module Networks
By Seung Wook Kim, Makarand Tapaswi, Sanja Fidler
Three Mechanisms of Weight Decay Regularization
By Guodong Zhang, Chaoqi Wang, Bowen Xu, Roger Grosse
LanczosNet: Multi-Scale Deep Graph Convolutional Networks
By Renjie Liao, Zhizhen Zhao, Raquel Urtasun, Richard Zemel
Excessive Invariance Causes Adversarial Vulnerability
By Joern-Henrik Jacobsen, Jens Behrmann, Richard Zemel, Matthias Bethge
Neural Graph Evolution: Automatic Robot Design
By Tingwu Wang, Yuhao Zhou, Sanja Fidler, Jimmy Ba
DOM-Q-NET: Grounded RL on Structured Language
By Sheng Jia, Jamie Ryan Kiros, Jimmy Ba
TimbreTron: A WaveNet(CycleGAN(CQT(Audio))) Pipeline for Musical Timbre Transfer
By Sicong Huang, Qiyang Li, Cem Anil, Xuchan Bao, Sageev Oore, Roger B. Grosse
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models
By Marc T Law, Jake Snell, Amir-massoud Farahmand, Raquel Urtasun, Richard S Zemel
Aggregated Momentum: Stability Through Passive Damping
By James Lucas, Shengyang Sun, Richard Zemel, Roger Grosse
Self-Tuning Networks: Bilevel Optimization of Hyperparameters using Structured Best-Response Functions
By Matthew Mackay, Paul Vicol, Jonathan Lorraine, David Duvenaud, Roger Grosse
Graph HyperNetworks for Neural Architecture Search
By Chris Zhang, Mengye Ren, Raquel Urtasun
Functional Variational Bayesian Neural Networks
By Shenyang Sun, Guodong Zhang, Jiaxin Xi, Roger Grosse.
FFJORD: Free-Form Continuous Dynamics for Scalable Reversible Generative Models
By Will Grathwohl, Ricky T. Q. Chen, Jesse Bettencourt, Ilya Sutskever, David Duvenaud
NIPS 2018
Neural Guided Constraint Logic Programming for Program Synthesis
By Lisa Zhang · Gregory Rosenblatt · Ethan Fetaya · Renjie Liao · William Byrd · Matthew Might · Raquel Urtasun · Richard Zemel
Learning Latent Subspaces in Variational Autoencoders
By Jack Klys · Jake Snell · Richard Zemel
Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer
By David Madras · Toni Pitassi · Richard Zemel
Reversible Recurrent Neural Networks
By Matthew MacKay · Paul Vicol · Jimmy Ba · Roger Grosse
On the Convergence and Robustness of Training GANs with Regularized Optimal Transport
By Maziar Sanjabi · Jimmy Ba · Meisam Razaviyayn · Jason Lee
Neural Ordinary Differential Equations
By Tian Qi Chen · Yulia Rubanova · Jesse Bettencourt · David Duvenaud
Isolating Sources of Disentanglement in Variational Autoencoders
By Tian Qi Chen · Xuechen Li · Roger Grosse · David Duvenaud
Iterative Value-Aware Model Learning
By Amir-massoud Farahmand
A Neural Compositional Paradigm for Image Captioning
By Bo Dai · Sanja Fidler · Dahua Lin
Online Structure Learning for Feed-Forward and Recurrent Sum-Product Networks
By Abdullah Rashwan · Agastya Kalra · Pascal Poupart · Prashant Doshi · Georgios Trimponias · Wei-Shou Hsu
Monte-Carlo Tree Search for Constrained POMDPs
By Jongmin Lee · Geon-hyeong Kim · Pascal Poupart · Kee-Eung Kim
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
By Vikash Goel · Jameson Weng · Pascal Poupart
Deep Homogeneous Mixture Models: Representation, Separation, and Approximation
By Priyank Jaini · Pascal Poupart · Yaoliang Yu
Data-dependent PAC-Bayes priors via differential privacy
By Gintare Karolina Dziugaite · Daniel Roy
Nearly tight sample complexity bounds for learning mixtures of Gaussians via sample compression schemes
By Hassan Ashtiani, Shai Ben-David, Nick Harvey, Chris Liaw, Abbas Mehrabian, Yaniv Plan
Non-convex Optimization with Discretized Diffusions
By Murat A Erdogdu, Lester Mackey, Ohad Shamir
Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
By Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, Timothy Lillicrap
Occam’s razor is insufficient to infer the preferences of irrational agents
By Stuart Armstrong, Sören Mindermann
Machine Learning for Health (ML4H): Moving beyond supervised learning in healthcare
Andrew Beam · Tristan Naumann · Marzyeh Ghassemi · Matthew McDermott · Madalina Fiterau · Irene Y Chen · Brett Beaulieu-Jones · Michael Hughes · Farah Shamout · Corey Chivers · Jaz Kandola · Alexandre Yahi · Samuel G Finlayson · Bruno Jedynak · Peter Schulam · Natalia Antropova · Jason Fries · Adrian Dalca
ICML 2018
Neural Relational Inference for Interacting Systems
By Thomas Kipf · Ethan Fetaya · Kuan-Chieh Wang · Max Welling · Richard Zemel
Distilling the Posterior in Bayesian Neural Networks
By Kuan-Chieh Wang · Paul Vicol · James Lucas · Li Gu · Roger Grosse · Richard Zemel
Reviving and Improving Recurrent Back-Propagation
By Renjie Liao · Yuwen Xiong · Ethan Fetaya · Lisa Zhang · KiJung Yoon · Zachary S Pitkow · Raquel Urtasun · Richard Zemel
Learning Adversarially Fair and Transferable Representations
By David Madras · Elliot Creager · Toniann Pitassi · Richard Zemel
Inference Suboptimality in Variational Autoencoders
By Chris Cremer · Xuechen Li · David Duvenaud
Noisy Natural Gradient as Variational Inference
By Guodong Zhang · Shengyang Sun · David Duvenaud · Roger Grosse
Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control
By Yangchen Pan · Amir-massoud Farahmand · Martha White · Saleh Nabi · Piyush Grover · Daniel Nikovski
Differentiable Compositional Kernel Learning for Gaussian Processes
By Shengyang Sun · Guodong Zhang · Chaoqi Wang · Wenyuan Zeng · Jiaman Li · Roger Grosse
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
By Gintare Karolina Dziugaite · Daniel Roy
Learning to Reweight Examples for Robust Deep Learning
By Mengye Ren · Wenyuan Zeng · Bin Yang · Raquel Urtasun
Nature: International Journal of Science
Observation of topological phenomena in a programmable lattice of 1,800 qubits
By Andrew D. King, Juan Carrasquilla, Jack Raymond, Isil Ozfidan, Evgeny Andriyash, Andrew Berkley, Mauricio Reis, Trevor Lanting, Richard Harris, Fabio Altomare, Kelly Boothby, Paul I. Bunyk, Colin Enderud, Alexandre Fréchette, Emile Hoskinson, Nicolas Ladizinsky, Travis Oh, Gabriel Poulin-Lamarre, Christopher Rich, Yuki Sato, Anatoly Yu. Smirnov, Loren J. Swenson, Mark H. Volkmann1, Jed Whittaker1, Jason Yao1, Eric Ladizinsky, Mark W. Johnson, Jeremy Hilton & Mohammad H. Amin
Machine learning for MEG during speech tasks
By Demetres Kostas, Elizabeth W. Pang & Frank Rudzicz
CVPR 2018
Now You Shake Me: Towards Automatic 4D Cinema
By Yuhao Zhou, Makarand Tapaswi and Sanja Fidler
Spotlight 3330
MovieGraphs: Towards Understanding Human-Centric Situations from Videos
By Paul Vicol, Makarand Tapaswi, Lluís Castrejón and Sanja Fidler
Spotlight 3308
Efficient Interactive Annotation of Segmentation Datasets with Polygon-RNN++
By David Acuna, Huan Ling, Amlan Kar and Sanja Fidler
Poster 3409
Learning to Act Properly: Predicting and Explaining Affordances from Images
By Ching-Yao Chuang, Jiaman Li, Antonio Torralba and Sanja Fidler
Poster 631
SurfConv: Bridging 3D and 2D Convolution for RGBD Images
By Hang Chu, Wei-Chiu Ma, Kaustav Kundu, Raquel Urtasun and Sanja Fidler
Poster 711
A Face to Face Neural Conversation Model
By Hang Chu and Sanja Fidler
Poster 710
Now You Shake Me: Towards Automatic 4D Cinema
By Yuhao Zhou, Makarand Tapaswi and Sanja Fidler
Poster 3330
VirtualHome: Simulating Household Activities via Programs
By Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler and Antonio Torralba
Poster 3399
MovieGraphs: Towards Understanding Human-Centric Situations from Videos
By Paul Vicol, Makarand Tapaswi, Lluís Castrejón and Sanja Fidler
Poster 3308
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
By Fabien Baradel, Christian Wolf, Julien Mille, and Graham Taylor
Poster 182
VirtualHome: Simulating Household Activities via Programs
By Xavier Puig, Kevin Ra, Marko Boben, Jiaman Li, Tingwu Wang, Sanja Fidler and Antonio Torralba
Oral 3399
ICLR 2018
Kronecker-factored Curvature Approximations for Recurrent Neural Networks
By James Martens, Jimmy Ba, Matt Johnson
Quantitatively Evaluating GANs With Divergences Proposed for Training
By Daniel Jiwoong Im, Alllan He Ma, Graham W. Taylor, Kristin Branson
Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches
By Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, Roger Grosse
Attacking Binarized Neural Networks
By Angus Galloway, Graham W. Taylor, Medhat Moussa
Meta-Learning for Semi-Supervised Few-Shot Classification
By Mengye Ren, Sachin Ravi, Eleni Triantafillou, Jake Snell, Kevin Swersky, Josh B. Tenenbaum, Hugo Larochelle, Richard S. Zemel
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
By Will Grathwohl, Dami Choi, Yuhuai Wu, Geoff Roeder, David Duvenaud
Understanding Short-Horizon Bias in Stochastic Meta-Optimization
By Yuhuai Wu, Mengye Ren, Renjie Liao, Roger Grosse
NerveNet: Learning Structured Policy with Graph Neural Networks
By Tingwu Wang, Renjie Liao, Jimmy Ba, Sanja Fidler
Predict Responsibly: Increasing Fairness by Learning to Defer
By David Madras, Toniann Pitassi, Richard Zemel
Isolating Sources of Disentanglement in Variational Autoencoders
By Tian Qi Chen, Xuechen Li, Roger Grosse, David Duvenaud
Graph Partition Neural Networks for Semi-Supervised Classification
By Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander Gaunt, Raquel Urtasun, Richard S. Zemel
Gradient-based Optimization of Neural Network Architecture
By Will Grathwohl, Elliot Creager, Seyed Ghasemipour, Richard Zemel
Reconstructing evolutionary trajectories of mutations in cancer
By Yulia Rubanova, Ruian Shi, Roujia Li, Jeff Wintersinger, Amit Deshwar, Nil Sahin, Quaid Morris
Inference in probabilistic graphical models by Graph Neural Networks
By KiJung Yoon, Renjie Liao, Yuwen Xiong, Lisa Zhang, Ethan Fetaya, Raquel Urtasun, Richard Zemel, Xaq Pitkow
Leveraging Constraint Logic Programming for Neural Guided Program Synthesis
By Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Raquel Urtasun, Richard Zemel
Talks
Deep Learning and Reinforcement Learning Summer School 2018
Katherine A. Heller, Department of Statistical Science, Duke University
Title: Introduction to Machine Learning
Video
Hugo Larochelle, Google
Title: Neural Networks I
Video
David Duvenaud, Department of Computer Science, University of Toronto
Title: Autodiff
Video
Hugo Larochelle, Google
Title: Neural Networks II
Video
Jonathon Shlens, Google
Title: Introduction to Convolutional Neural Networks (CNNs)
Video
Sanja Fidler, Department of Computer Science, University of Toronto
Title: Advanced Deep Vision
Video
Balázs Kégl, Laboratoire de l Accélérateur Linéaire, University of Paris-Sud 11
Title: RAMP (Practical session)
Video
David Duvenaud, Department of Computer Science, University of Toronto
Title: Generative Models I
Video
Been Kim, Google
Title: Interpretability
Video
Sanjeev Arora, Department of Computer Science, Princeton University
Title: Theory
Video
Jimmy Ba, Department of Computer Science, University of Toronto
Title: Optimization I
Video
Jorge Nocedal, Robert R. McCormick School of Engineering and Applied Science, Northwestern University
Title: Optimization II
Video
Yoshua Bengio, Department of Computer Science and Operations Research, University of Montreal
Title: Recurrent Neural Networks (RNNs)
Video
Graham Neubig, Language Technologies Institute, Carnegie Mellon University
Title: Language Understanding
Video
Jamie Kiros, Department of Computer Science, University of Toronto
Title: Multimodal Learning
Video
Blake Aaron Richards, Centre for the Neurobiology of Stress (CNS), University of Toronto Scarborough
Title: Computational Neuroscience
Video
Andrew Gordon Wilson, Department of Computer Science, Cornell University
Title: Bayesian Neural Nets
Video
Sageev Oore, Vector Institute
Title: Deep Learning and Music
Video
Richard S. Sutton, Department of Computing Science, University of Alberta
Title: Introduction to RL and TD
Video
Sergey Levine, Department of Electrical Engineering and Computer Sciences, UC Berkeley
Title: Policy Search
Video
Amir-massoud Farahmand, Vector Institute
Title: Batch RL and ADP
Video
Martha White, Department of Computing Science, University of Alberta
Title: Off-Policy Learning
Video
Tor Lattimore, DeepMind Technologies Limited
Title: Bandits and Explore/Exploit in RL
Video
Doina Precup, School of Computer Science, McGill University
Title: Temporal Abstraction
Video
Emma Brunskill, Computer Science Department, Carnegie Mellon University
Title: Multi-task and Transfer in RL
Video
Marc G. Bellemare, Google
Title: Deep RL
Video
Hal Daumé III, Department of Computer Science, University of Maryland
Title: Imitation Learning
Video
Mohammad Ghavamzadeh, INRIA Lille – Nord Europe
Title: Safety in RL
Video
Michael Bowling, Department of Computing Science, University of Alberta
Title: Multi-agent RL
Video
VECTOR INSTITUTE MACHINE LEARNING ADVANCES AND APPLICATIONS SEMINAR
Presented at The Fields Institute
Presented at Fields Institute
Kyunghyun Cho, New York University
Three Recent Directions in Neural Machine Translation (Video)
Abstract
Research Sit Down with Kyunghyun Cho and Roger Grosse
Special Edition: Alan Aspuru-Guzik, Jimmy Ba, Murat Erdogu and Marzyeh Ghassemi, Vector Institute
Special Edition: Research Overviews (Video)
Mikhail Belkin, Ohio State University
Fit without Fear: an Interpolation Perspective on Generalization and Optimization in Modern Machine Learning (Video)
Abstract
Kevin Murphy, Google Research
From Perception to Prediction (Video)
Abstract
Research Sit Down with Kevin Murphy and Richard Zemel
Ryan Adams, Princeton University
Unbiased Estimation of the Eigenvalues of Large Implicit Matrices (Video)
Abstract
Phil Blunsom, Deepmind, Oxford University
Modelling Structure in Language (Video)
Abstract
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
Presented at Fields Institute
David Sontag, Massachusetts Institute of Technology
How is Machine Learning Going to Change Health Care? (Video)
Abstract
Alex Graves, DeepMind
Frontiers in Recurrent Neural Network Research
Abstract
Max Welling, University of Amsterdam
Generalizing convolutions for Deep Learning (Video)
Abstract
Ernest Earon, Precision Hawk
Airborne Intelligence: Why birds are so good at what they do (Video)
Abstract
James Bergstra, Kindred
From Teleoperation to AGI (Video)
Abstract
Ryan Geriepy, ClearPath Robotics
3 Steps To Applying Machine Learning To Fleets Of Self-Driving Vehicles (Video)
Abstract
Shane Gu, Cambridge University
Sample-Efficient Deep Reinforcement Learning for Robotics (Video)
Abstract
David Blei, Columbia University
Probabilistic Topic Models and User Behavior (Video)
Abstract
Fernanda Viegas, Martin Wattenberg and Daniel Smilkov, Google
Visualization for machine learning–and human learning, too (Video)
Abstract
Roger Melko, University of Waterloo
Machine Learning Quantum Physics (Video)
Abstract
Pieter Abbeel, University of California, Berkeley
Deep Reinforcement Learning for Robotics (Video)
Abstract
Roger Grosse, University of Toronto
Optimizing neural networks using structured probabilistic models (Video)
Abstract
Graham Taylor, University of Guelph
Dataset Augmentation in Feature Space (Video)
Abstract
Rob Fergus, New York University / Facebook
Memory and Communication in Neural Networks (Video)
Abstract
Hugo Larochelle, Google Brain
Autoregressive Generative Models with Deep Learning (Video)
Abstract
Michael Schull, Institute for Clinical Evaluative Sciences
ICES: Linking data and discovery, and building a health data science agenda (Video)
Abstract
Alexandre Le Bouthillier, Imagia
Imagia: Artificial Intelligence for medical image analysis (Video)
Abstract
Brendan Frey, Deep Genomics
Deep Genomics: Changing the course of genomic medicine (Video)
Abstract
Joshua Landy, Figure 1
Figure 1: The computer will see you now (Video)
Abstract
Ofer Shai, Meta Inc.
Meta: Unlocking the world’s scientific and technical insights (Video)
Abstract
Roger Grosse, David Duvenaud and Sanja Fidler,, University of Toronto
Meet the New Faculty: Roger Grosse, David Duvenaud & Sanja Fidler (Video)
Abstract
Andrew McCallum, University of Massachusetts
Universal Schema for Representation and Reasoning from Natural Language (Video)
Abstract
Richard Sutton, University of Alberta
The Future of Artificial Intelligence Belongs to Search and Learning (Video)
Abstract
Geoffrey Hinton, University of Toronto
Using Fast Weights to Store Temporary Memories (Video)
Abstract
Amir Ban, Tel-Aviv University
When Should an Expert Make a Prediction? (Video)
Abstract
VECTOR FRIDAY ML SEMINAR
Yaoliang Yu, University of Toronto
Deep Homogeneous Mixture Models: Representation, Separation and Approximation (Video)
Introduction
Vector Institute
Sheila McIlraith, University of Toronto
High-level Reward Function Specification in Reinforcement Learning (Video)
Introduction
Vector Institute
Babak Taati, University of Toronto
Introduction
Vector Institute
Yuri Boykov, University of Toronto
Image Segmentation without Full Supervision (Video)
Vector Institute
Gillian Hadfield, University of Toronto
AI Alignment and Human Normativity (Video)
Introduction
Vector Institute
Scott Sanner, University of Toronto
Autoencoders for Collaborative Filtering (Video)
Introduction
Vector Institute
Peter Wittek, University of Toronto
Machine Learning on Near-Term Quantum Computers (Video)
Introduction
Vector Institute
Jesse Hoey, University of Waterloo
Affective Dynamics and Control in Group Processes (Video)
Introduction
Vector Institute
Andreas Moshovos, University of Toronto
Value-Based Deep Learning Hardware Acceleration (Video)
Introduction
Vector Institute
Micheal Brudno, University of Toronto
Improving Doctor-Patient Interaction with AI-Enabled Medical Note Taking (Video)
Introduction
Vector Institute
HEALTH AI ROUNDS
Bartha Knoppers, McGill University
Data Sharing and the Human Right to Benefit From Science and its Applications (Video)
Vector Institute
Yin Aphinyanaphongs, NYU Langone Health
Clinical Predictive Analytics at NYU Langone Health (Video)
Introduction
Vector Institute
Samantha Kleinberg, Stevens Institute of Technology
Large-scale causal inference in bio-medicine (Video)
Introduction
Vector Institute
Responsible AI
Presented at Centre of Ethics, University of Toronto
Richard Zemel, University of Toronto
Ensuring Fair and Responsible Automated Decisions (Video)
Centre of Ethics, University of Toronto
Frank Rudzicz, University of Toronto, University Health Network, Winterlight Labs
The Future of Automated Healthcare (Video)
Centre of Ethics, University of Toronto
Taking Responsibility for Responsible AI
On August 26, Join the Vector Institute and the Schwartz Reisman Institute for Technology and Society hosted Chris Meserole of the Brookings Institution to explore opportunities to collaborate on advancing the Vector Institute’s vision to use AI to foster economic growth and improve the lives of Canadians.
Canada was the first country to announce a national AI strategy in 2017. The world quickly followed suit. The same year, China announced its intent to become the world’s AI leader by 2030 and shortly after, the United States launched the American AI Initiative.
Recognizing the transformative potential of AI, organizations are evolving to handle complex interdisciplinary questions. MIT launched the Schwarzman College and Stanford established the Institute for Human-Centered Artificial Intelligence (HAI). Companies are hiring ethicists, developing ethical AI principles, and investing in related initiatives.
Right here in Toronto, the largest ever donation to the University of Toronto will establish the Schwartz Reisman Institute for Technology and Society to harness strengths across disciplines. And governments are exploring digital governance principles.
Canada’s concentration of world-class machine learning scientists presents a major opportunity to turn knowledge into economic competitiveness. What will the roles of governments and businesses be in maximizing Canada’s economic opportunity while protecting Canadians’ rights and values? Will we be leaders or laggards?
Speakers:
Taking Responsibility for Responsible AI
Gillian Hadfield: Faculty Affiliate, Vector Institute; Inaugural Director, Schwartz Reisman Institute for Technology and Society and Schwartz Reisman Chair in Technology and Society; Professor of Law and Strategic Management at the University of Toronto Rotman School of Management
Topic: Overview: Schwartz-Reisman Institute for Technology and Society
Graham Taylor: Canada CIFAR AI Chair and Faculty Member, Vector Institute; Academic Director, University of Guelph Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI); Associate Professor and Canada Research Chair in Machine Learning, School of Engineering, University of Guelph; Visiting Faculty, Google Brain Montreal (until 2019-05); Academic Director, NextAI
Topic: Overview: University of Guelph Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI)
David Madras: PhD Student in the Machine Learning Group at the University of Toronto and the Vector Institute
Topic: Machine learning in decision-making systems
Aleksandar Nikolov: Professor, Department of Computer Science, University of Toronto; Canada Research Chair in Algorithms and Private Data Analysis
Topic: Differential Privacy: Rigorously Private Data Analysis
Sheila McIlraith: Faculty Affiliate, Vector Institute; Professor, Department of Computer Science, University of Toronto
Topic: AI Safety
Melissa McCradden: Postdoctoral Fellow in Ethics of AI in Healthcare, Department of Bioethics and Genetics & Genome Biology at The Hospital for Sick Children and Vector Institute
Research interests: Translation of AI tools into clinical care, neuroethics, paediatric bioethics, and sport ethics
Shalmali Joshi: Postdoctoral Fellow, Vector Institute
Topic: Towards safe deployment of (clinical) machine learning models
Frank Rudzicz: Canada CIFAR AI Chair and Faculty Member, Vector Institute; Director of AI, Surgical Safety Technologies Incorporated; Scientist, International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St Michael’s Hospital; Associate Professor, Department of Computer Science, University of Toronto
Topic: AI safety, explainable AI in the operating room, standards for evaluating ML models
Charles Morgan: President, International Technology Law Association (ITechLaw); Partner, McCarthy Tétrault
Topic: ITech Law’s recently published “Responsible AI: A Global Policy Framework”
Chris Meserole: Fellow in Foreign Policy at the Brookings Institution and Deputy Director of the Brookings Artificial Intelligence and Emerging Technology Initiative
Advancing AI: A Framework and Agenda for Cross-Disciplinary Research
Abstract: Technical breakthroughs in deep learning architectures, such as capsule networks and transformers, continue to drive forward the state-of-the-art in AI performance. Likewise, research on the ethical, legal, and social implications (ELSI) of AI has also advanced rapidly in recent years, most notably the work on ethical principles. Yet most AI scholarship remains relatively siloed, despite the urgent need for work informed by both technical and non-technical fields. This talk aims to sketch a brief framework and agenda for cross-disciplinary research on AI.