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Machine Learning

Deep Learning on Graphs: History, Successes, Challenges, and Next Steps

By: 
Michael Bronstein

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases, has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [2], if not two [3], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the Machine Learning (ML) community.

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Military operations and training present a broad variety of demanding physical tasks which may impact the Warfighter physical performance and health. As it is for anyone who exercises intensely, the possibility of injury is always lurking around the corner.

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: November 7, 2018
Chapter:Tokyo/Fukuoka/Hiroshima/ Nagoya/<br />Sapporo/Shikoku/ Shin-Etsu Joint Chapter
Chapter Chair: Shoji Makino
Topic: Hyperspectral Unmixing: Insights and Beyond

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: June 1 & 7, 2018
Chapter: France 
Chapter Chair: William Puech
Topic: (1) Hyperspectral Unmixing in Remote Sensing: Learn the
Wisdom There and Go Beyond (Machine Learning Included)
(2) MIMO Transceiver Designs and Optimization: Beyond Beamforming and
Perfect Channel Information

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: June 5, 2018
Chapter: Benelux 
Chapter Chair: Francois Horlin
Topic: Hyperspectral Unmixing in Remote Sensing: Learn the
Wisdom There and Go Beyond (Machine Learning Included)

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