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Date: 9 March 2023
Time: 9:00 AM ET (New York Time)
Presenter(s): Mr. Sayantan Dutta
Date: 13 February 2023
Time: 10:30 AM ET (New York Time)
Title: Human Centric Visual Analysis - Hand, Gesture, Pose, Action, and Beyond
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Date: 18 January 2023
Time: 3:00 PM (Paris Time)
Title: Robustness of Graph Neural Networks
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Submission Deadline: 15 June 2023
Call for Proposals Document
Date: 15 February 2023
Time: 10:00 AM ET (New York Time)
Presenter(s): Mr. Wei Liu, Dr. Li Chen and Dr. Wenyi Zhang
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Date: 19 January 2023
Time: 1:00 PM ET (New York Time)
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We are advertising for a two-year Post-Doc position at King’s College London to work with Prof. Osvaldo Simeone and Prof. Bipin Rajendran on AI and neuromorphic computing with applications to wireless communication systems. Of particular interest is the investigation, at fundamental theoretical and algorithmic levels, of digital twin platforms for the design and monitoring of next-generation cellular systems.
First, I would like to wish you and your loved ones a nice new year filled with health and happiness. The last few years have been challenging for various reasons: the COVID-19 pandemic, climatic events, and the war in Ukraine, to name a few. It seems impossible to be able to stop the megalomania and madness of some human beings.
Recent years have witnessed a rapidly growing interest in next-generation imaging systems and their combination with machine learning. While model-based imaging schemes that incorporate physics-based forward models, noise models, and image priors laid the foundation in the emerging field of computational sensing and imaging, recent advances in machine learning, from large-scale optimization to building deep neural networks, are increasingly being applied in modern computational imaging.
The compressive sensing (CS) scheme exploits many fewer measurements than suggested by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has attracted considerable attention in the computational imaging community. While classic image CS schemes employ sparsity using analytical transforms or bases, the learning-based approaches have become increasingly popular in recent years. Such methods can effectively model the structure of image patches by optimizing their sparse representations or learning deep neural networks while preserving the known or modeled sensing process.