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Skip to Search Results- 144Machine Learning
- 19Artificial Intelligence
- 17Reinforcement Learning
- 15Deep Learning
- 10Computer Vision
- 10Natural Language Processing
- 2Wen, Junfeng
- 1Aghaei, Nikoo
- 1Al Dallal, Ahmed
- 1Al-Masri, Mohammad
- 1Alam Anik, Md Tanvir
- 1Alateeq, Majed Mohammad
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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...
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Fall 2016
The field of biomedicine is reeling from “information overload”. Indeed, biomedical researchers find it almost impossible to stay current with published literature due to the vast amounts of data being generated and published. As a result, they are turning to text mining. Over the past two...
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Fall 2022
The objective of signal decomposition is to extract and separate distinct signal components from a composite signal. Signal decomposition has been studied in many applications, such as image, video, audio, and speech signals. This thesis focuses on the category of signal decomposition on...
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Fall 2011
This thesis studies the reinforcement learning and planning problems that are modeled by a discounted Markov Decision Process (MDP) with a large state space and finite action space. We follow the value-based approach in which a function approximator is used to estimate the optimal value function....
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Spring 2020
Reinforcement learning (RL) has received wide attention in various fields lately. Model-free RL brings data-driven solutions that learn the control strategy directly from interaction with process data without the need for a process model. This is especially beneficial in the case of nonlinear...
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Robust and Accurate Generic Visual Object Tracking Using Deep Neural Networks in Unconstrained Environments
DownloadSpring 2022
The availability of affordable cameras and video-sharing platforms have provided a massive amount of low-cost videos. Automatic tracking of objects of interest in these videos is the essential step for complex visual analyses. As a fundamental computer vision task, Visual Object Tracking aims at...
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Fall 2013
Many learning situations involve learning the conditional distribution $p(y|x)$ when the training data is drawn from the training distribution $p{tr}(x)$, even though it will later be used to predict for instances drawn from a different test distribution $p{te}(x)$. Most current approaches focus...
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Scalable Solutions to Image Abnormality Detection and Restoration using Limited Contextual Information
DownloadFall 2020
Detecting and interpreting image abnormalities and restoring images are essential to many processing pipelines in diverse fields. Challenges involved include randomness and unstructured nature of image artefacts (from signal processing perspective) and performance constraints imposed by...
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Fall 2017
Real-time strategy (RTS) games are war simulation video games in which the players perform several simultaneous tasks like gathering and spending resources, building a base, and controlling units in combat against an enemy force. RTS games have recently drawn the interest of the game AI research...
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Fall 2018
Neural approaches to sequence labeling often use a Conditional Random Field (CRF) to model their output dependencies, while Recurrent Neural Networks (RNN) are used for the same purpose in other tasks. We set out to establish RNNs as an attractive alternative to CRFs for sequence labeling. To do...