2021 IEEE 31st International Workshop on Machine Learning for Signal Processing MLSP

活动简介

The 31st MLSP workshop in the series of workshops organized by the IEEE Signal Processing Society MLSP Technical Committee will take place in Gold Coast, Australia. If conditions allow, we plan to have a conference running in a hybrid format with a virtual program for attendees that are not able to attend the conference and in person attendance for attendees that can attend. The conference will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, special and regular single-track sessions as well as matchmaking events. The presented papers will be published in and indexed by IEEE Xplore.

Sponsor Type:1

组委会

General Chairs

• Abd-Krim Seghouane, University of Melbourne
• Mohammed Bennamoun, University of Western Australia
• Jonathan Manton, University of Melbourne

Program Chairs

• Dong Xu, University of Sydney
• Liang Zheng, Australian National University
• Wen Li, University of Electronic Science and Technology Chine

Plenary Chairs

• Ba-ngu Vo, Curtin University
• Tongliang Liu, University of Sydney

Tutorial Chairs

• Hamid Laga, Murdoch University
• Qian Yu, Beihang University

Special session Chair

• Lu Sheng, Beihang University

Finance Chair

• Luping Zhou, University of Sydney

Student Prize Chair

• Chunhua Shen, University of Adelaide

Data Competition Chairs

• Ercan Kuruoglu, Consiglio Nazionale delle Ricerche
• Danilo Comminiello, Sapienza University of Rome

Publicity Chair

• Navid Shokouhi, Global Kinetics

Publication Chairs

• Guo Lu, Beijing Institute of Tectnology
• Jing Zhang, Beihang University

征稿信息

重要日期

2021-06-14

初稿截稿日期

2021-08-31

初稿录用日期

征稿范围

Theoretical and application Topics:

• Learning theory and algorithms
• Information-theoretic learning
• Deep learning techniques
• Distributed/Federated learning
• Dictionary learning
• Graphical and kernel methods
• Learning from multimodal data
• Independent component analysis
• Matrix factorizations/completion
• Reinforcement learning
• Transfer learning
• Source separation
• Reinforcement learning
• Subspace and manifold learning
• Sequential learning
• Self-supervised and semi-supervised learning
• Tensor-based signal processing
• Sparsity-aware processing
• Pattern recognition and classification
• Music and audio processing
• Applications of machine learning