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Physical Sciences and Mathematics Commons™
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- Machine learning (2)
- Ad allocation (1)
- Artificial intelligence (1)
- Bipartite graphs (1)
- Bright light therapy (1)
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- Circadian rhythms (1)
- Data science (1)
- Decision making (1)
- Diffusion approximation (1)
- Entrainment maps (1)
- Graph theory (1)
- Major depressive disorder (1)
- Markov operator (1)
- Melatonin (1)
- Mood disorders (1)
- Neural network (1)
- Online matching (1)
- Probabilistic algorithms (1)
- Stationary probability distributions (1)
- Stochastic gradient descent (1)
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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
A Survey On Online Matching And Ad Allocation, Ryan Lee
A Survey On Online Matching And Ad Allocation, Ryan Lee
Theses
One of the classical problems in graph theory is matching. Given an undirected graph, find a matching which is a set of edges without common vertices. In 1990s, Richard Karp, Umesh Vazirani, and Vijay Vazirani would be the first computer scientists to use matchings for online algorithms [8]. In our domain, an online algorithm operates in the online setting where a bipartite graph is given. On one side of the graph there is a set of advertisers and on the other side we have a set of impressions. During the online phase, multiple impressions will arrive and the objective of …
Bright Light Therapy And Depression: Assessing Suitability Using Entrainment Maps, Charles A. Mainwaring
Bright Light Therapy And Depression: Assessing Suitability Using Entrainment Maps, Charles A. Mainwaring
Theses
Bright Light Therapy has been shown to be efficacious to mood disorders including Major Depression. Researchers use the Jewett-Forger-Kronauer model of the circadian rhythm with the Unified Model of melatonin including a mathematical term implementing feedback from the melatonin system into the circadian system to quantify the effects of bright light. Early investigations into intrinsic period, light sensitivity, and the circadian pacemaker's sensitivity to blood melatonin concentration may be indicators of subsets of patients with long intrinsic periods exhibiting symptoms of depression.
Stationary Probability Distributions Of Stochastic Gradient Descent And The Success And Failure Of The Diffusion Approximation, William Joseph Mccann
Stationary Probability Distributions Of Stochastic Gradient Descent And The Success And Failure Of The Diffusion Approximation, William Joseph Mccann
Theses
In this thesis, Stochastic Gradient Descent (SGD), an optimization method originally popular due to its computational efficiency, is analyzed using Markov chain methods. We compute both numerically, and in some cases analytically, the stationary probability distributions (invariant measures) for the SGD Markov operator over all step sizes or learning rates. The stationary probability distributions provide insight into how the long-time behavior of SGD samples the objective function minimum.
A key focus of this thesis is to provide a systematic study in one dimension comparing the exact SGD stationary distributions to the Fokker-Planck diffusion approximation equations —which are commonly used in …
Analysis Of Gameplay Strategies In Hearthstone: A Data Science Approach, Connor W. Watson
Analysis Of Gameplay Strategies In Hearthstone: A Data Science Approach, Connor W. Watson
Theses
In recent years, games have been a popular test bed for AI research, and the presence of Collectible Card Games (CCGs) in that space is still increasing. One such CCG for both competitive/casual play and AI research is Hearthstone, a two-player adversarial game where players seeks to implement one of several gameplay strategies to defeat their opponent and decrease all of their Health points to zero. Although some open source simulators exist, some of their methodologies for simulated agents create opponents with a relatively low skill level. Using evolutionary algorithms, this thesis seeks to evolve agents with a higher skill …