Deep Learning Computer Vision
ORAL · Q32 · ID: 48673
Presentations
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A neural network can hear the shape of a drum
ORAL
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Presenters
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Yueqi Zhao
UC San Diego, University of California, San Diego
Authors
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Yueqi Zhao
UC San Diego, University of California, San Diego
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Michael M Fogler
University of California, San Diego
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Deepfake Video Detection Using Biologically Inspired Geometric Deep Learning
ORAL
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Presenters
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Jansen Wong
Great Neck South High School
Authors
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Steven Luo
Evergreen Valley High School
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Jansen Wong
Great Neck South High School
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Yash Agarwal
Dougherty Valley High School
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Aayush Sheth
Tesla STEM High School
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Krish Jain
Redmond High School
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Eric Zhu
Princeton High School
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Eric Guan
North Carolina School of Science and Mathematics
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Nilesh Chaturvedi
Department of Applied Mathematics and Statistics, Stony Brook University
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Pawel Polak
Department of Applied Mathematics and Statistics, Stony Brook University
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Unsupervised Machine Learning for Spatio-Temporal Characterization of Nanoscale Phenomena Imaged via Ultrafast Electron Microscopy
ORAL
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Presenters
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Thomas E Gage
Argonne National Laboratory
Authors
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Faran Zhou
Argonne National Laboratory
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Thomas E Gage
Argonne National Laboratory
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Haihua Liu
Argonne National Laboratory
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Ilke Arslan
Argonne National Laboratory
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Haidan Wen
Argonne National Laboratory
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Maria K Chan
Argonne National Laboratory
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Semantic Segmentation for Analysis of Melting of Nanoscale Ice via Fully Convolutional Neural Networks
ORAL
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Presenters
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Arun Baskaran
Argonne National Laboratory
Authors
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Arun Baskaran
Argonne National Laboratory
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Yulin Lin
Argonne National Laboratory
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Jianguo Wen
Argonne National Laboratory
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Maria K Chan
Argonne National Laboratory
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One Visualization is Worth 1000 Words: Toward Automated Data Recovery and Interpretation from Past 3D Visualizations
ORAL
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Publication: This work builds on Brandt, L.E. and Freeman, W.T. (2021) Toward Automatic Interpretation of 3D Plots.
Presenters
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Laura E Brandt
MIT CSAIL
Authors
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Laura E Brandt
MIT CSAIL
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William T Freeman
MIT CSAIL, NSF IAIFI
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Lithium Metal Battery Characterization using X-ray Imaging and Machine Learning
ORAL
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Publication: [1] Noack, Zwart, Ushizima, Fukuto, Yager, Elbert, Murray, Stein, Doerk, Tsai, Li, Freychet, Zhernenkov, Holman, Lee, Chen, Rotenberg, Weber, Le Goc, Boehm, Steffens, Mutti, Sethian, "Gaussian Processes for Autonomous Data Acquisition at Large-Scale X-Ray and Neutron Scattering Facilities", Nature Reviews Physics, 2021.<br>[2] Ushizima, McCormick, Parkinson, "Accelerating Microstructural Analytics with Dask for Volumetric X-ray Imaging", PyHPC, 9th Workshop on Python for High-Performance and Scientific Computing, Supercomputing Nov 2020.<br>[3] Ushizima and Noack, Gaussian Processes and Deep Learning for Experimental Data, Machine Learning and Data in Polymer Physics II, APS March Meeting 2021.<br>[4] Siqueira, Ushizima, van der Walt, Large-scale segmentation using fully convolutional neural networks, https://arxiv.org/pdf/2101.04823.pdf.
Presenters
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Daniela Ushizima
Lawrence Berkeley National Laboratory, UC Berkeley, UC San Francisco
Authors
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Daniela Ushizima
Lawrence Berkeley National Laboratory, UC Berkeley, UC San Francisco
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Ying Huang
National Fuel Cell Research Center, UC Irvine
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Jerome Quenum
UC Berkeley, Lawrence Berkeley National Laboratory
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David Perlmutter
Lawrence Berkeley National Laboratory
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Dilworth Parkinson
Lawrence Berkeley National Laboratory
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Iryna Zenyuk
National Fuel Cell Research Center, UC Irvine
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