Videos

Name: Alberta Machine Intelligence Institute (Amii)
Address: 2nd Floor, 10065 Jasper Ave, Edmonton, Alberta, T5J 3B1, Canada

Sep 24, 2020
"Tea Time Talks 2020: Matthew Schlegel, A first look at hierarchical predictive coding "
Sep 22, 2020
AI Seminar: Cynthia Rudin on Simpler Models and How to Find Them
Sep 22, 2020
AI Seminar: Craig Sherstan on Representation and General Value Functions
Sep 17, 2020
AI Meetup: Explainable AI & Reinforcement Learning
Sep 17, 2020
"Tea Time Talks 2020: Martha Steenstrup, Control of Communications Networks - Tryin' to Make it RL Compared to What? "
Sep 17, 2020
Tea Time Talks 2020: Han Wang, Emergent Representations in Reinforcement Learning and their Properties
Sep 17, 2020
Tea Time Talks 2020: Yufeng Yuan, Multimodal Observation Space for Robot Learning
Sep 17, 2020
Tea Time Talks 2020: Parash Rahman, Stochastic Gradient Descent in a Changing World
Sep 8, 2020
AI Seminar: Emilie Kaufmann on Learning Good Policies with and Without Rewards
Sep 4, 2020
Tea Time Talks 2020: Shibhansh Dohare, The Interplay of Search and Gradient Descent in Semi-stationary Learning Problems
Aug 17, 2020
Tea Time Talks: Connor Stephens, How to Sample When No One’s Watching – Open Problems in Bandit Exploration
Aug 17, 2020
Tea Time Talks: Rory Dawson, Adaptive Switching for Improved Control of Robotic Prosthesis
Jul 31, 2020
AI Seminar: Yixing Luan on Translations for Word Sense Disambiguation
Jul 30, 2020
"Tea Time Talks: Negar Hassanpour, Counterfactual Reasoning in Observational Studies "
Jul 30, 2020
Tea Time Talks: Yangchen Pan, An Implicit Function Learning Approach for Parametric Modal Regression
Jul 16, 2020
AI Meetup: G2V Optics & Vector Institute
Jul 15, 2020
AI Seminar: Neeraj Kumar on Computational Pathology
Jun 23, 2020
AI in Health: The Future of Research & Application
Jun 23, 2020
Tea Time Talks: Martha White, Policy Gradient Methods as Approximate Policy Iteration
Jun 23, 2020
Tea Time Talks: Patrick Pilarski, On Time
Jun 23, 2020
Tea Time Talks: Csaba Szepesvári, Embracing Approximation in RL
Jun 23, 2020
Tea Time Talks: Rich Sutton, Are You Ready to Fully Embrace Approximation?
Jun 19, 2020
AI Seminar: Kevin Leyton-Brown on Making an Impact in AI Projects
Jun 18, 2020
AI Meetup: RLAI Lab & BLINC Lab | Amii

Videos

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Content Type

Video

"Tea Time Talks 2020: Matthew Schlegel, A first look at hierarchical predictive coding "

AI Seminar: Cynthia Rudin on Simpler Models and How to Find Them

AI Seminar: Craig Sherstan on Representation and General Value Functions

AI Meetup: Explainable AI & Reinforcement Learning

"Tea Time Talks 2020: Martha Steenstrup, Control of Communications Networks - Tryin' to Make it RL Compared to What? "

Tea Time Talks 2020: Han Wang, Emergent Representations in Reinforcement Learning and their Properties

Tea Time Talks 2020: Yufeng Yuan, Multimodal Observation Space for Robot Learning

Tea Time Talks 2020: Parash Rahman, Stochastic Gradient Descent in a Changing World

AI Seminar: Emilie Kaufmann on Learning Good Policies with and Without Rewards

Tea Time Talks 2020: Shibhansh Dohare, The Interplay of Search and Gradient Descent in Semi-stationary Learning Problems

Tea Time Talks: Connor Stephens, How to Sample When No One’s Watching – Open Problems in Bandit Exploration

Tea Time Talks: Rory Dawson, Adaptive Switching for Improved Control of Robotic Prosthesis

AI Seminar: Yixing Luan on Translations for Word Sense Disambiguation

"Tea Time Talks: Negar Hassanpour, Counterfactual Reasoning in Observational Studies "

AI Meetup: G2V Optics & Vector Institute

AI Seminar: Neeraj Kumar on Computational Pathology

AI in Health: The Future of Research & Application

Tea Time Talks: Martha White, Policy Gradient Methods as Approximate Policy Iteration

Tea Time Talks: Patrick Pilarski, On Time

Tea Time Talks: Csaba Szepesvári, Embracing Approximation in RL

Tea Time Talks: Rich Sutton, Are You Ready to Fully Embrace Approximation?

AI Seminar: Kevin Leyton-Brown on Making an Impact in AI Projects

AI Meetup: RLAI Lab & BLINC Lab | Amii

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"Tea Time Talks 2020: Matthew Schlegel, A first look at hierarchical predictive coding "

AI Seminar: Cynthia Rudin on Simpler Models and How to Find Them

AI Seminar: Craig Sherstan on Representation and General Value Functions

AI Meetup: Explainable AI & Reinforcement Learning

"Tea Time Talks 2020: Martha Steenstrup, Control of Communications Networks - Tryin' to Make it RL Compared to What? "

Tea Time Talks 2020: Han Wang, Emergent Representations in Reinforcement Learning and their Properties

Tea Time Talks 2020: Yufeng Yuan, Multimodal Observation Space for Robot Learning

Tea Time Talks 2020: Parash Rahman, Stochastic Gradient Descent in a Changing World

AI Seminar: Emilie Kaufmann on Learning Good Policies with and Without Rewards

Tea Time Talks 2020: Shibhansh Dohare, The Interplay of Search and Gradient Descent in Semi-stationary Learning Problems

Tea Time Talks: Connor Stephens, How to Sample When No One’s Watching – Open Problems in Bandit Exploration

Tea Time Talks: Rory Dawson, Adaptive Switching for Improved Control of Robotic Prosthesis

AI Seminar: Yixing Luan on Translations for Word Sense Disambiguation

"Tea Time Talks: Negar Hassanpour, Counterfactual Reasoning in Observational Studies "

Tea Time Talks: Yangchen Pan, An Implicit Function Learning Approach for Parametric Modal Regression

AI Meetup: G2V Optics & Vector Institute

AI Seminar: Neeraj Kumar on Computational Pathology

AI in Health: The Future of Research & Application

Tea Time Talks: Martha White, Policy Gradient Methods as Approximate Policy Iteration

Tea Time Talks: Patrick Pilarski, On Time

Tea Time Talks: Csaba Szepesvári, Embracing Approximation in RL

Tea Time Talks: Rich Sutton, Are You Ready to Fully Embrace Approximation?

AI Seminar: Kevin Leyton-Brown on Making an Impact in AI Projects

AI Meetup: RLAI Lab & BLINC Lab | Amii

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