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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 74Machine Learning
- 70Reinforcement Learning
- 41Artificial Intelligence
- 36Machine learning
- 22Natural Language Processing
- 22Reinforcement learning
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Fall 2009
Lookup tables are frequently used in many applications to store and retrieve keyvalue pairs. Designing efficient lookup tables can be challenging with constraints placed on storage, query response time and/or result accuracy. This thesis proposes Geometric filter, a lookup table with a space...
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Fall 2014
We study the problem of geotagging named entities where the goal is to identify the most relevant location of a named entity based on the content of the Web pages where the entity is mentioned. We hypothesize the relationship between the mentions of an entity and its geo-center in web pages, and...
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Fall 2014
After decades of research, gestural interfaces are becoming increasingly commonplace in our interactions with modern devices. They promise natural and efficient interaction, but suffer from a lack of affordances and thus require learning on the part of the user. This thesis examines the...
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Gesture Recognition using Hidden Markov Models, Dynamic Time Warping, and Geometric Template Matching
DownloadFall 2013
Gesture recognition is useful in many applications, including human-computer interaction, automated sign language recognition, medical applications, and many more. The main focus of this thesis is to improve the isolated gesture recognition accuracy of Hidden Markov Models (HMMs) and to provide a...
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Fall 2022
This thesis investigates a new approach to model-based reinforcement learning using background planning: mixing (approximate) dynamic programming updates and model-free updates, similar to the Dyna architecture. Background planning with learned models is often worse than model-free alternatives,...
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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...