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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
IP address lookup is an important processing function of Internet routers. The challenge lies in finding the longest prefix that matches the packet’s destination address. One of the issues concerning IP address lookups is the average lookup time. In previous works, caching was shown to be an...
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Spring 2024
As cancer is the leading global cause of death, an ongoing challenge is predicting an individual's cancer progression accurately, to facilitate personalized treatment planning. Individuals diagnosed with cancer may succumb to the illness or face cancer recurrence post-treatment. The first part of...
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Fall 2018
Microscopic image analysis is a broad term that covers the use of digital image processing techniques to process and analyze images obtained from a microscope. It is of significant interest to a number of diverse fields such as medicine, biological research, cancer research, drug testing, etc. A...
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Fall 2023
In reinforcement learning (RL), agents learn to maximize a reward signal using nothing but observations from the environment as input to their decision making processes. Whether the agent is simple, consisting of only a policy that maps observations to actions, or complex, containing auxiliary...
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Fall 2014
We study the problem of classifying users in a classified ad network and its applications in further analyzing the network. Specifically, we seek to classify Kijiji users into one of the two business and non-business categories. The problem is challenging due to the sparsity of the data about...
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Fall 2020
In this dissertation we consider the problem of ontology-based data access when the underlying ontology language is represented using tuple-generating dependencies (a term used in theory of databases), also known as existential rules (in the artificial intelligence literature). This problem is...
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Chasing Hallucinated Value: A Pitfall of Dyna Style Algorithms with Imperfect Environment Models
DownloadSpring 2020
In Dyna style algorithms, reinforcement learning (RL) agents use a model of the environment to generate simulated experience. By updating on this simulated experience, Dyna style algorithms allow agents to potentially learn control policies in fewer environment interactions than agents that use...