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Heterogeneous Multi-task Learning with Expert Diversity

Learning And Generalization

Learning Discriminative Prototypes with Dynamic Time Warping

Computer Vision

Uncertainty-Aware Action Advising for Deep Reinforcement Learning Agents

Reinforcement Learning

Towers of Saliency: A Reinforcement Learning Visualization Using Immersive Environments

Reinforcement Learning

Noise Flow: Noise Modeling with Conditional Normalizing Flows

Computer Vision

Lifelong GAN: Continual Learning for Conditional Image Generation

Learning And Generalization

Action Guidance with MCTS for Deep Reinforcement Learning

Reinforcement Learning

Terminal Prediction as an Auxiliary Task for Deep Reinforcement Learning

Reinforcement Learning

Agent Modeling as Auxiliary Task for Deep Reinforcement Learning

Reinforcement Learning

Private Q-Learning with Functional Noise in Continuous Spaces

Reinforcement Learning, Responsible AI

Skynet: A Top Deep RL Agent in the Inaugural Pommerman Team Competition

Reinforcement Learning

Uniform Stability and High Order Approximation of SGLD in Non-Convex Learning

Learning And Generalization

Machine Learning Certification: Approaches and Challenges

Responsible AI

Explainability I: local post-hoc explanations

Our NeurIPS 2021 Reading List

Computer Vision, Data Visualization, Graph Representation Learning, Learning And Generalization, Natural Language Processing, Optimization, Reinforcement Learning, Time series Modelling, Unsupervised Learning

Heterogeneous Multi-task Learning with Expert Diversity

Computer Vision, Natural Language Processing, Reinforcement Learning

Tutorial #9: SAT Solvers I: Introduction and applications

Optimization

The End of Anonymity Ensuring Facial Recognition is Used for Good

Notes on NIPS 2017

Learning And Generalization, Natural Language Processing

The Pommerman team competition or: How we learned to stop worrying and love the battle

Learning And Generalization

2020 End of Year Reading List

Our NeurIPS 2018 papers

Learning And Generalization, Reinforcement Learning, Responsible AI

Tutorial #4: auxiliary tasks in deep reinforcement learning

Reinforcement Learning

Is multiagent deep reinforcement learning the answer or the question? A brief survey

The Borealis AI 2020-2021 Fellowships: Supporting Canada’s AI Research Ecosystem

Prof. Kristian Kersting on deep machines that know when they do not know

Prof. Pascal Poupart on unsupervised video object segmentation for deep reinforcement learning

Xiaodan Zhu on several deep learning models for reasoning in natural language

Ioannis Mitliagkas on studying momentum dynamics for faster training, better scaling and easier tuning

Karolina Dziugaite on nonvacuous generalization bounds for deep neural networks via PAC-Bayes

Waterloo.ai heralds the next wave of Canada’s AI cities

Prof. Pascal Poupart on structured learning in deep learning

Prof. Greg Mori on deep structured models for human activity

Borealis AI co-hosts ‘AI in Toronto’ event for MILA summer school students

RESPECT AI: Right to some Privacy

Responsible AI

NIPS 2016