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.
Are you looking to energize signal processing students, early stage researchers, and industry practitioners? Consider hosting a virtual Seasonal School for young engineers!
Are you looking to energize signal processing students, early stage researchers, and industry practitioners? Consider hosting a virtual Seasonal School for young engineers!
Are you looking to energize signal processing students, early stage researchers, and industry practitioners? Consider hosting a virtual Seasonal School for young engineers!
The Signal Processing Society (SPS) has 12 Technical Committee that support a broad selection of signal processing-related activities defined by the scope of the Society.
Three new Members-at-Large will take their seats on the IEEE Signal Processing Society Board of Governors beginning 1 January 2021 and will serve until 31 December 2023. Nine candidates competed for the three Member-at-Large positions. These successful candidates represent a broad spectrum of the IEEE Signal Processing Society.
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Prof. Sharon Gannot
Ramat-Gan, Israel
for contributions to acoustical modelling and statistical learning in speech enhancement
Dr. Jingdong Chen
Xi'an, China
for contributions to microphone array processing and speech enhancement in noisy and reverberant
environments
Yifan Gong
Redmond, WA, USA
for leadership in creating cloud speech recognition services in industry
Lori Lamel
Orsay, France