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Name:	High Dimensional Deeplearning (HDL2020)
Description:	Higher dimensional data such as 3D, video, and simulation data are a leading edge of pattern recognition research. With the growth of prevalent application areas such as 3D games, self-driving automobiles, automobile and airplane design, health monitoring and sports activity training, a wide variety of new sensors and simulation techniques have allowed researchers to develop feature description models beyond 2D. In this workshop, we will present an overview and key insights into the state of the art of higher dimensional features from a wide variety of techniques including but not limited to deep learning and also traditional approaches. For example, numerous current pattern recognition methods are using 3D information from the sensor (e.g. KINECT, LIDAR, MRI, …) or are using 3D in modeling and understanding the 3D world. Although higher dimensional data enhance the performance of methods on numerous tasks, they can also introduce new challenges and problems. The higher dimensionality of the data often leads to more complicated structures which present additional problems in both extracting meaningful content and in adapting it for current machine learning algorithms.
PC Chairs:	Veysel Kocaman
	Dr. Bas van Stein
	Michael Lew
	Markus Olhofer
Conference flow:	Abstract Submission: October 10, 2020 23:59 CEST,
	Paper Upload: October 10, 2020 23:59 CEST,
	Assignment of Reviewers: October 12, 2020 23:59 CEST,
	Review: November 6, 2020 23:59 CET,
	Decision: November 10, 2020 23:59 CET,
	Final: November 15, 2020 23:59 CET

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