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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson
Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson
The Summer Undergraduate Research Fellowship (SURF) Symposium
In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, …
Problems In Graph-Structured Modeling And Learning, James Atwood
Problems In Graph-Structured Modeling And Learning, James Atwood
Doctoral Dissertations
This thesis investigates three problems in graph-structured modeling and learning. We first present a method for efficiently generating large instances from nonlinear preferential attachment models of network structure. This is followed by a description of diffusion-convolutional neural networks, a new model for graph-structured data which is able to outperform probabilistic relational models and kernel-on-graph methods at node classification tasks. We conclude with an optimal privacy-protection method for users of online services that remains effective when users have poor knowledge of an adversary's behavior.