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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 83Machine Learning
- 76Reinforcement Learning
- 42Artificial Intelligence
- 37Machine learning
- 24Natural Language Processing
- 23reinforcement learning
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Spring 2015
Believable, realistic video game character behaviour continues to lag behind the improvements in graphics, stories and game play in video games. In this dissertation we focus on the use of two techniques, emotional gaits and emotional incidents, as a way to add easily identifiable, non-verbal,...
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Fall 2011
We present a new family of gradient temporal-difference (TD) learning methods with function approximation whose complexity, both in terms of memory and per-time-step computation, scales linearly with the number of learning parameters. TD methods are powerful prediction techniques, and with...
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Fall 2020
Computing a Nash equilibrium in zero-sum games, or more generally saddle point optimization, is a fundamental problem in game theory and machine learning, with applications spanning across a wide variety of domains, from generative modeling and computer vision to super-human AI in imperfect...
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Fall 2011
Data is growing at an unforeseen rate, with TBs being generated daily. A large part of this data is unstructured in nature. This has pushed the traditional techniques of storing it in relational databases to its limit and new alternatives are necessary. Cloud databases have emerged as a viable...
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Fall 2019
An accurate model of a patient’s individual survival distribution can help determine the appropriate treatment for terminal patients. Unfortunately, risk scores (e.g., from Cox Proportional Hazard models) do not provide survival probabilities, single-time probability models (e.g., the Gail model,...