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Do Not Learn If You Trust Yourself: Efficient Resource-Constrained Online Learning Using Uncertainty

Tešnar, Michal (2024) Do Not Learn If You Trust Yourself: Efficient Resource-Constrained Online Learning Using Uncertainty. Bachelor's Thesis, Artificial Intelligence.


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Machine learning proves effective in constructing dynamics models from data, especially for underwater vehicles. Continuous refinement of these models using incoming data streams, however, often requires storage of an overwhelming amount of redundant data. This thesis investigates the use of uncertainty in the selection of data points to rehearse in online learning when storage capacity is constrained. The models are learned using an ensemble of multilayer perceptrons as they perform well at predicting epistemic uncertainty. We present three novel approaches: the Threshold method, which excludes samples with uncertainty below a specified threshold, the Greedy method, designed to maximize uncertainty among the stored points, and Threshold-Greedy, which combines the previous two approaches. The methods are assessed on data collected by an underwater vehicle Dagon. Comparison with baselines reveals that the Threshold exhibits enhanced stability throughout the learning process and also yields a final model with the lowest testing loss. We also conducted detailed analyses on the impact of model parameters and storage size on the performance of the models.

Item Type: Thesis (Bachelor's Thesis)
Supervisor name: Valdenegro Toro, M.A.
Degree programme: Artificial Intelligence
Thesis type: Bachelor's Thesis
Language: English
Date Deposited: 29 Feb 2024 12:29
Last Modified: 29 Feb 2024 12:29

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