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Supervisors
- 22White, Martha (Computing Science)
- 3White, Adam (Computing Science)
- 1Bowling, Michael (Computing Science)
- 1Farahmand, Amir-massoud (Computer Science, University of Toronto)
- 1Fyshe, Alona (Computing Science)
- 1Greiner, Russell (Computing Science)
Author / Creator / Contributor
Subject / Keyword
- 10Reinforcement Learning
- 5Machine Learning
- 3Neural Networks
- 2Dyna
- 2Model-based Reinforcement Learning
- 2Reinforcement learning
Year
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Item type
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Fall 2019
In this thesis, we investigate different vector step-size adaptation approaches for continual, online prediction problems. Vanilla stochastic gradient descent can be considerably improved by scaling the update with a vector of appropriately chosen step-sizes. Many methods, including AdaGrad,...
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Fall 2023
Oftentimes, machine learning applications using neural networks involve solving discrete optimization problems, such as in pruning, parameter-isolation-based continual learning and training of binary networks. Still, these discrete problems are combinatorial in nature and are also not amenable to...
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