The Tea Time Talks 2021: Week Four

The Tea Time Talks are back! Throughout the summer, take in 20-minute talks on early-stage ideas, prospective research and technical topics delivered by students, faculty and guests. Presented by Amii and the RLAI Lab at the University of Alberta, the talks are a relaxed and informal way of hearing leaders in AI discuss future lines of research they may explore.

Watch select talks from the fourth week of the series now:

Dhawal Gupta: Structural Credit Assignment in Neural Networks Using Reinforcement Learning

Abstract: Structural credit assignment in neural networks is a long-standing problem, with a variety of alternatives to backpropagation proposed to allow for local training of nodes. One of the early strategies was to treat each node as an agent and use a reinforcement learning method called REINFORCE to update each node locally with only a global reward signal. In this talk, Dhawal revisits this approach and investigates if we can leverage other reinforcement learning approaches to improve learning.

Khurram Javed: Towards Scalable Real-time Representation Learning

Abstract: In this talk, Khurram motivates the need for scalable real-time learning algorithms and presents one instantiation of an algorithm that allows us to learn deep hierarchical features in a scalable way. The central idea behind the algorithm is to build a deep recurrent network over-time. The algorithm uses shallow gradient-based learning to build new features and tests their usefulness by using them to make predictions for the task at hand. He demonstrates the effectiveness of the proposed approach on a simple on-policy prediction task that requires both non-linear feature construction and memory.

Like what you’re learning here? Take a deeper dive into the world of RL with the Reinforcement Learning Specialization, offered by the University of Alberta and Amii. Taught by Martha White and Adam White, this specialization explores how RL solutions help solve real-world problems through trial-and-error interaction, showing learners how to implement a complete RL solution from beginning to end. Enroll in this specialization now!

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