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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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Fall 2021
Ponnamperuma Arachchige, Ayantha Randika
Optical character recognition (OCR) is a widely used pattern recognition application in numerous domains. Several feature-rich commercial OCR solutions and opensource OCR solutions are available for consumers, which can provide moderate to excellent accuracy levels. These solutions are...
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Fall 2023
The recognition performance of Optical Character Recognition (OCR) models can be sub-optimal when document images suffer from various degradations. Supervised learning-based methods for image enhancement can generate high-quality enhanced images. However, these methods require the availability of...
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Fall 2021
Deep neural network (DNN) has been developed rapidly in years. While it shows promising results in various tasks of computer vision, DNN typically suffers from accuracy loss due to the domain shift from a source domain to a target domain. To mitigate the accuracy loss without the label from...
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Fall 2018
This thesis seeks to contribute to the ongoing research on opinion mining. The contributions are related to the development of newly conceived models for discovery of the viewpoints, and the reasons supporting them, from various polarized contentious texts found in surveys' responses, debate...
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Spring 2024
Syntactic text simplification, the task of reducing the grammatical complexity of text while preserving the content, can be useful for non-native speakers, text summarization, and other downstream natural language processing tasks. Many traditional methods are rule-based and do not generalize,...
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Fall 2022
Sentence reconstruction and generation are essential applications in Natural Language Processing (NLP). Early studies were based on classic methods such as production rules and statistical models. Recently, the prevailing models typically use deep neural networks. In this study, we utilize deep...
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Spring 2020
Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary...