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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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Spring 2024
Bengali and Hind are two widely spoken yet low-resource languages. The state-of-the-art in modeling such languages uses BERT and the Wordpiece tokenizer. We observed that the Wordpiece tokenizer often breaks words into meaningless tokens, failing to separate roots from affixes. Moreover,...
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Fall 2017
Modern board, card, and video games are challenging domains for AI research due to their complex game mechanics and large state and action spaces. For instance, in Hearthstone — a popular collectible card (CC) (video) game developed by Blizzard Entertainment — two players first construct their...
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Improving Deep Deterministic Policy Gradient for Sparse Reward and Goal-Conditioned Continuous Control
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We propose an improved version of deep deterministic policy gradient (DDPG) for sparse reward and goal-conditioned reinforcement learning. To enhance exploration, we introduce \emph{${\epsilon}{t}$-greedy}, which uses search to generate exploratory options, focusing on less-visited states. We...
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Spring 2019
Current state-of-the-art algorithms for trick-taking card games use a process called determinization. Determinization is a technique that allows the application of perfect information state evaluation algorithms to imperfect information games. It involves a two-step process in which a perfect...
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Improving Different Aspects in RL - Accelerating Convergence Rate & Enhancing Safety and Robustness
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Reinforcement learning (RL) has moved from toy domains to real-world applications, while each of these applications has inherent difficulties which are long-standing challenges in RL, such as: stucking at plateaus, limited training time, costly exploration and safety considerations. I, with my...
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Fall 2018
Image quality assessment (IQA) algorithms aim to simulate human judgment of visual quality on an image. These algorithms are essential components of every multimedia pipeline. IQA is divided into full reference(FR) or no reference (NR) depending on the presence or absence of a pristine image...
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Fall 2021
A common scientific challenge for putting a reinforcement learning agent into practice is how to improve sample efficiency as much as possible with limited computational or memory resources. Such available physical resources may vary in different applications. My thesis introduces some approaches...
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Spring 2018
Semantic segmentation is about classifying every pixel in an image. In recent years, methods based on Fully Convolutional Networks (FCN) have dominated this field in terms of segmentation accuracy. We are interested in tackling the challenges that these methods are faced with. First, it is...