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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 2Rabbany khorasgani, Reihaneh
- 2Sacharuk, Edward, 1948-
- 2Sharifi, AmirAli
- 54Machine Learning
- 48Reinforcement Learning
- 37Artificial Intelligence
- 31Machine learning
- 20Image processing. Digital techniques.
- 18Artificial intelligence
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Spring 2022
As a student learns to program, there will be gaps in the student's knowledge that must be addressed for the student to gain a full understanding of the material. A student's answer to a single question may provide some insight into the student's level of understanding. However, a well-chosen...
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Spring 2022
In this dissertation, we study online off-policy temporal-difference learning algorithms, a class of reinforcement learning algorithms that can learn predictions in an efficient and scalable manner. The contributions of this dissertation are one of the two kinds: (1) empirically studying existing...
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Spring 2022
The world offers unprecedented amounts of data in real-world domains, from which we can develop successful decision-making systems. It is possible for reinforcement learning (RL) to learn control policies offline from such data but challenging to deploy an agent during learning in safety-critical...
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Spring 2022
Boolean Satisfiability (SAT) is a well-known NP-complete problem. Despite the theoretical hardness of SAT, backtracking search based Conflict Directed ClauseLearning (CDCL) SAT solvers can solve very large real-world SAT instances with surprising efficiency. The high efficiency of CDCL SAT...
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Insights into Early Word Comprehension - Tracking the Neural Representations of Word Semantics in Infants
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Infants start developing rudimentary language skills and can start understanding simple words well before their first birthday. This development has also been shown primarily using Event Related Potential (ERP) techniques to find evidence of word comprehension in the infant brain. While these...
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Spring 2022
Named Entity Disambiguation (NED) and linking has been traditionally evaluated on natural language content that is both well-written and contextually rich. However, many NED approaches display poor performance on text sources that are short and noisy. In this thesis, we study the problem of...
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An Empirical Study on Learning and Improving the Search Objective for Unsupervised Paraphrasing
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Research in unsupervised text generation has been gaining attention over the years. One recent approach is local search towards a heuristically defined objective, which specifies language fluency, semantic meanings, and other task-specific attributes. Search in the sentence space is realized by...
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Spring 2022
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution. In this work, focusing on fixed design linear regression with Gaussian noise and a...
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Fall 2022
The rise of Deep Learning (DL) and its assistance in learning complex feature representations significantly impacted Reinforcement Learning (RL). Deep Reinforcement Learning (DRL) made it possible to apply RL to complex real-world problems and even achieve human-level performance. One of these...
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Fall 2022
Answering information-seeking question involves retrieving relevant documents from a massive haystack of unstructured text corpora. This dissertation aims at building question answering (QA) systems that can be deployed in the wild where incoming questions may be noisy and their distribution...