Title	Artificially intelligent S/TEM
Creator	Xin, Huolin
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-10-20T09:00
Description	Deep learning introduces the potential for autonomous S/TEM characterization, a step towards unsupervised data acquisition and analysis through machine learning. Whole image classification is the first step towards the long-term goal of autonomous image data acquisition and analysis. We have successfully retrained AlexNet, b-FCN, and U-Net, pre-existing deep learning Convolutional Neural Networks(CNNs), for autonomous, whole image classification and analysis, on TEM image datasets.
Extent	43 minutes
Subject	Mathematics; Numerical analysis; Approximations and expansions; Control/optimization/operation research
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: Brookhaven National Laboraotory
Series	BIRS Workshop Lecture Videos (Oaxaca de Juárez (Mexico))
Date Available	2018-04-19
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0365803
URI	http://hdl.handle.net/2429/65504
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Other
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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