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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 2011
The goal of top-k ranking is to rank individuals so that the best k of them can be determined. Depending on the application domain, an individual can be a person, a product, an event, or just a collection of data or information for which an ordering makes sense. In the context of databases, top-k...
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Fall 2018
With rapidly increasing user-generated geo-tagged content in social media, location-based queries have received more attention lately. There has been extensive work on finding top-K frequent terms in specific locations from social network data streams. However, the problem reverse spatial term...
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Fall 2022
Topic modelling seeks to uncover the conceptual and thematic content of collections of documents. These topics can be used as features for document indexing and classification. However, topic models are increasingly important as tools of applied research. As we seek to develop agents capable of...
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Spring 2024
This work aims to address the lack of clear theoretical foundations in computational lexical semantics, the sub-field of natural language processing pertaining to computing with the meaning of words. Semantic tasks are of interest for end-user applications (e.g. contextual translation),...
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Spring 2021
Emphatic-Temporal-Difference (Emphatic-TD) learning algorithms were recently proposed based on the most central and widely used reinforcement learning algorithms, Temporal-Difference (TD) methods. Emphatic-TD learning algorithms were originally designed to solve the divergence problem of...
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Spring 2021
The backpropagation algorithm is a fundamental algorithm for training modern artificial neural networks (ANNs). However, it is known the backpropagation algorithm performs poorly on changing problems. We demonstrate the backpropagation algorithm can perform poorly on a clear, generic, changing...
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Toward Practical Reinforcement Learning Algorithms: Classification Based Policy Iteration and Model-Based Learning
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In this dissertation, we advance the theoretical understanding of two families of Reinforcement Learning (RL) methods: Classification-based policy iteration (CBPI) and model-based reinforcement learning (MBRL) with factored semi-linear models. In contrast to generalized policy iteration, CBPI...
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Fall 2024
In this thesis, we investigate the problem of robustness and resilience in simultaneous localization and mapping systems (SLAM). With the vast adoption of robotics in many industries and disciplines, robustness and resilience are becoming of immense importance for the reliable and safe deployment...
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Fall 2018
Knowledge bases (KBs), repositories consisting of entities, facts about entities, and relations between entities, are a vital component for many tasks in artificial intelligence and natural language processing such as semantic search and question answering. Named Entity Disambiguation (NED), the...