Dissertation/Thesis Abstract

Deep Learning for Puzzles and Circadian Rhythms
by Agostinelli, Forest, Ph.D., University of California, Irvine, 2019, 99; 27663716
Abstract (Summary)

The combination of deep learning with reinforcement learning and the application of deep learning to the sciences is a relatively new and flourishing field. We show how deep reinforcement learning techniques can learn to solve problems, often in the most efficient way possible, when faced with many possibilities but little information by designing an algorithm that can learn to solve seven different combinatorial puzzles, including the Rubik's cube. Furthermore, we show how deep learning can be applied to the field of circadian rhythms. Circadian rhythms are fundamental for all forms of life. Using deep learning, we can gain insight into circadian rhythms on the molecular level. Finally, we propose new deep learning algorithms that yield significant performance improvements on computer vision and high energy physics tasks.

Indexing (document details)
Advisor: Baldi, Pierre
Commitee: Shahbaba, Babak, Singh, Sameer
School: University of California, Irvine
Department: Computer Science - Ph.D.
School Location: United States -- California
Source: DAI-B 81/10(E), Dissertation Abstracts International
Subjects: Artificial intelligence, Bioinformatics
Keywords: Artificial intelligence, Bioinformatics, Circadian rhythms, Deep learning, Rubik's cube, Search
Publication Number: 27663716
ISBN: 9798607316815
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