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Showing results 18 to 35 of 35 < previous 
Issue DateTitleAuthor(s)
Dec-2019Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTXWoods, B. J. Q.; Duarte, V. N.; Fredrickson, E. D.; Gorelenkov, N. N.; Podestà, M.; Vann, R. G. L.
Dec-2019Machine Learning Characterization of Alfvénic and Sub-Alfvénic Chirping and Correlation With Fast-Ion Loss at NSTXWoods, B. J. Q.; Duarte, V. N.; Fredrickson, E. D.; Gorelenkov, N. N.; Podestà, M.; Vann, R. G. L.
2020Machine learning for multi-subject fMRI analysisZhang, Hejia
2014Machine Learning on GraphsEis, David Jeremy
2017Mapping Sub-Saharan African Agriculture in High-Resolution Satellite Imagery with Computer Vision & Machine LearningDebats, Stephanie Renee
2019MCMC algorithms for sampling from multimodal and changing distributionsLee, Holden
2016Multi-scale adaptive representation of signals: models and algorithmsTai, Cheng
2017Non-convex Optimization for Machine Learning: Design, Analysis, and UnderstandingMa, Tengyu
2018Nonconvex Statistical OptimizationWang, Zhaoran
2016Optimal and Adaptive Online LearningLuo, Haipeng
2017Part I. Engaging Iminium Ions and Radicals in Nickel Catalysis. Part II. Predictive Reaction Modeling Using Machine Learning.Ahneman, Derek
2013Provable Algorithms for Machine Learning ProblemsGe, Rong
2017Relaxing the Implementation of Embedded Sensing Systems through Machine Learning and Statistical OptimizationWang, Zhuo
2015Scalable inference of discrete data: user behavior, networks and genetic variationGopalan, Prem Krishna
2019Strategies for predicting, understanding, and controlling colloidal crystallizationReinhart, Wesley F
2016Targeted analyses of very large genome-wide data collectionsLee, Young-suk
2017The Study of Complex Behavioral Changes with Age in Drosophila melanogaster Using a Quantitative Framework for Behavioral DescriptionChoi, Daniel Moon-hyung
May-2020Toward fusion plasma scenario planning for NSTX-U using machine-learning-accelerated modelsMark D. Boyer