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Skip to Search Results- 28Greiner, Russell (Computing Science)
- 22White, Martha (Computing Science)
- 3White, Adam (Computing Science)
- 2Schuurmans, Dale (Computing Science)
- 1Bolduc, Francois (Pediatrics)
- 1Bowling, Michael (Computing Science)
- 13Machine Learning
- 11Reinforcement Learning
- 9Machine learning
- 3Neural Networks
- 3Reinforcement learning
- 2Artificial Intelligence
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Spring 2010
This thesis presents Pathway Informed Analysis (PIA), a classification method for predicting disease states (diagnosis) from metabolic profile measurements that incorporates biological knowledge in the form of metabolic pathways. A metabolic pathway describes a set of chemical reactions that...
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Spring 2022
A key problem in the theory of meta-learning is to understand how the task distributions influence transfer risk, the expected error of a meta-learner on a new task drawn from the unknown task distribution. In this work, focusing on fixed design linear regression with Gaussian noise and a...
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Addressing the Challenges of Applying Machine Learning for Predicting Mental Disorders and Their Prognosis Using Two Case Studies
DownloadSpring 2019
Ghoreishiamiri, Seyedehreyhaneh
One of the principal applications of machine learning in psychiatry is to build automated tools that can help clinicians predict the diagnosis and prognosis of mental disorders using available data from patients’ profiles. Here, in two different studies, we investigate ways to use machine learn-...
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Spring 2020
Reinforcement Learning is a formalism for learning by trial and error. Unfortunately, trial and error can take a long time to find a solution if the agent does not efficiently explore the behaviours available to it. Moreover, how an agent ought to explore depends on the task that the agent is...
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Spring 2022
Policy gradient (PG) estimators are ineffective in dealing with softmax policies that are sub-optimally saturated, which refers to the situation when the policy concentrates its probability mass on sub-optimal actions. Sub-optimal policy saturation may arise from a bad policy initialization or a...
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Spring 2019
Forouzandehmoghadam, Amirhosein
A biomarker is a feature (e.g., gene expression, SNP, etc.) that is significantly different between two classes of instances – typically case and control. Knowing these biomarkers can help us understand a biological condition or identify the appropriate treatment for a certain disease. Many...
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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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Building an expert-system based conversational agent to provide personalised resources about neurological disorders
DownloadSpring 2022
Researchers developing artificially intelligent conversational agents (aka, chat- bots) seek effective ways to provide personal assistance to users with various needs. We have implemented a web-based conversational agent that recom- mends resources to help clients (caregivers of patients...
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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...
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Spring 2016
One of the key obstacles to the effective use of mass spectrometry (MS) in high throughput metabolomics is the difficulty in interpreting measured spectra to accurately and efficiently identify metabolites. Traditional methods for automated metabolite identification compare the target MS spectrum...