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Skip to Search Results- 83Machine Learning
- 19Artificial Intelligence
- 17Reinforcement Learning
- 9Natural Language Processing
- 8Deep Learning
- 5Computer Vision
- 2Jacobsen, Andrew
- 2Wen, Junfeng
- 1Aghaei, Nikoo
- 1Alam Anik, Md Tanvir
- 1Ashley, Dylan R
- 1Ashrafi Asli, Seyed Arad
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Spring 2020
The predictive representations hypothesis is that representing the state of the world in terms of predictions about the future will result in good generalization. In this thesis, good generalization is specifically quantified by good learning performance in both accuracy and speed when predicting...
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Assessing the Feasibility of Learning Biomedical Phenotype Patterns Using High-Throughput Omics Profiles
DownloadSpring 2014
A decade after the completion of the human genome project, the rapid advancement of the high-throughput measurement technologies has made omics (genomics, epigenomics, transcriptomics, metabolomics) profiling feasible. The availability of such omics profiles has raised the hope for the...
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Fall 2012
Automated sports commentary is a form of automated narrative and human-computer interaction. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We introduce a system called the...
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Fall 2018
Videogames often use artificial intelligence to control characters in the game world. In doing so, videogames require one or more agents to navigate from their current location to some desired goal location without collisions. We explore improving algorithm performance in both single and...
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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 2024
Reinforcement learning has been widely applied in different control tasks. However, its performance often faces the challenge of low sample efficiency. Introducing human prior knowledge is often seen as a possible solution, such as behaviour cloning, learning from advice, and inverse...
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Fall 2024
This thesis presents a novel data-driven approach for identifying categoryselective regions in the human brain that are consistent across multiple participants. By leveraging a massive fMRI dataset and a multi-modal (language and image) neural network (CLIP), we trained a highly accurate...
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Fall 2016
Current medical imaging professional training uses an apprenticeship model with students following an established doctor and viewing their cases, in what is called a practicum. This posses an issue as students are limited to the cases available during their practicum. To resolve this automated...
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Fall 2024
It has been shown that pretrained language models exhibit biases and social stereotypes. Prior work on debiasing these language models has largely focussed on modifying embedding spaces in pretraining, which is not scalable for large models. Since pretrained models are typically fine-tuned on...
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Fall 2012
Functional Magnetic Resonance Imaging (fMRI) measures the dynamic activity of each voxel of a brain. This dissertation addresses the challenge of learning a diagnostic classifier that uses a subject’s fMRI data to distinguish subjects with neuropsychiatric disorders from healthy controls. fMRI...