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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 2023
Many real-world tasks in fields such as robotics and control can be formulated as constrained Markov decision processes (CMDPs). In CMDPs, the objective is usually to optimize the return while ensuring some constraints being satisfied at the same time. The primal-dual approach is a common...
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Fall 2014
Underwater sensor networks (UWSNs) have recently attracted increasing research attention for their potential use in supporting many important applications and services. Examples include scientific applications such as studies of marine life, industrial applications such as monitoring underwater...
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Fall 2016
In recent years, Underwater Sensor Networks (UWSNs) have attracted attention for their potential use in many applications. To name a few, UWSNs have been considered in studying marine life, oceanographic data collection, monitoring underwater oil pipelines, and a variety of military and homeland...
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Probabilistic Methods for Discrete Labeling Problems in Digital Image Processing and Analysis
DownloadFall 2012
Many problems in digital image processing and analysis can be interpreted as labeling problems, which aim to find the optimal mapping from a set of sites to a set of labels. A site represents a certain primitive, such as a pixel, while a label represents a certain quantity, such as disparity in...
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Spring 2013
This work introduces the “online probing” problem: In each round, the learner is able to purchase the values of a subset of features for the current instance. After the learner uses this information to produce a prediction for this instance, it then has the option of paying for seeing the full...