Journal ArticleDOI
Pattern Recognition and Machine Learning
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TLDR
This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.Abstract:
(2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.read more
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Journal ArticleDOI
Machine Learning in Seismology: Turning Data into Insights
Qingkai Kong,Daniel T. Trugman,Zachary E. Ross,Michael J. Bianco,Brendan J. Meade,Peter Gerstoft +5 more
TL;DR: Five research areas in seismology are surveyed in which ML classification, regression, clustering algorithms show promise: earthquake detection and phase picking, earthquake early warning, ground‐motion prediction, seismic tomography, and earthquake geodesy.
Journal ArticleDOI
An Overview of Artificial Intelligence Applications for Power Electronics
TL;DR: The three distinctive life-cycle phases, design, control, and maintenance are correlated with one or more tasks to be addressed by AI, including optimization, classification, regression, and data structure exploration.
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Clinical applications of the functional connectome
F. Xavier Castellanos,Adriana Di Martino,R. Cameron Craddock,R. Cameron Craddock,Ashesh D. Mehta,Michael P. Milham,Michael P. Milham +6 more
TL;DR: Evidence of the readiness of R-fMRI based functional connectomics to lead to clinically meaningful biomarker identification is assessed through the lens of the criteria used to evaluate clinical tests (i.e., validity, reliability, sensitivity, specificity, and applicability).
Journal ArticleDOI
Election Campaigning on Social Media: Politicians, Audiences, and the Mediation of Political Communication on Facebook and Twitter
TL;DR: In this article, the authors focus on the use of social media platforms in political communication and examine how politicians use different platforms in their campaigns, focusing on the German federal election in 2017.
Posted Content
Edward: A library for probabilistic modeling, inference, and criticism
TL;DR: Edward enables the development of complex probabilistic models and their algorithms at a massive scale and builds on top of TensorFlow to support distributed training and hardware such as GPUs.