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Skip to Search Results- 2Abdi Oskouie, Mina
- 2Birkbeck, Neil Aylon Charles
- 2Cai, Zhipeng
- 2Chen, Jiyang
- 2Chowdhury, Md Solimul
- 2Chubak, Pirooz
- 83Machine Learning
- 76Reinforcement Learning
- 42Artificial Intelligence
- 37Machine learning
- 24Natural Language Processing
- 23reinforcement learning
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Fall 2019
Building Information Modeling (BIM) has become an integral part of the design process, as all building data is accessible in a digital representation and can be viewed in a 3D environment prior to construction. This supports the capability of evaluating or checking a model against building codes...
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Fall 2013
A type-based alias analysis uses the types of variables in a program to assist in determining the alias relations of those variables. The C standard imposes restrictions on the types of expressions that may access objects in memory, with the explicit intent of specifying when two objects may be...
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Sample-Efficient Control with Directed Exploration in Discounted MDPs Under Linear Function Approximation
DownloadSpring 2022
An important goal of online reinforcement learning algorithms is efficient data collection to learn near-optimal behaviour, that is, optimizing the exploration-exploitation trade-off to reduce the sample-complexity of learning. To improve sample-complexity of learning it is essential that the...
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Fall 2011
Propositional satisability (SAT) has been a dominant tool in solving some practical NP-complete problems. However, SAT also has a number of weaknesses, including its inability to compactly represent numerical constraints and its low level, unstructured language of clauses. In contrast, Constraint...
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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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Scale space feature selection with Multiple kernel learning and its application to oil sand image analysis
DownloadSpring 2012
Scale-space representation for an image is a significant way to generate features for object detection/classification. The size of the object we are looking for as well as its texture contents are related to the multi-scale representations. However, any scale-space based features face...