Call for Papers
2020 IEEE International Workshop on
Machine Learning for Signal Processing (MLSP)
Machine learning, as the driving force of this wave of AI, provides powerful solutions to many real-world technical and scientific challenges. The 30th MLSP workshop, an annual event organized by the IEEE Signal Processing Society MLSP Technical Committee, will present the most recent and exciting advances in machine learning for signal processing through keynote talks, tutorials, as well as special and regular single-track sessions. Prospective authors are invited to submit papers on relevant algorithms and applications including, but not limited to:
- Learning theory and modeling
- Neural networks and deep learning
- Bayesian Learning and modeling
- Sequential learning, sequential decision methods
- Information-theoretic learning
- Graphical and kernel models
- Bounds on performance
- Source separation and independent component analysis
- Signal detection, pattern recognition and classification
- Tensor and structured matrix methods
- Machine learning for big data
- Large scale learning
- Dictionary learning, subspace and manifold learning
- Semi-supervised and unsupervised learning
- Active and reinforcement learning
- Learning from multimodal data
- Resource efficient machine learning
- Cognitive information processing
- Bioinformatics applications
- Biomedical applications and neural engineering
- Speech and audio processing applications
- Image and video processing applications
- Intelligent multimedia and web processing
- Communications applications
- Other applications including social networks, games, smart grid, security and privacy
Special Session Call for Proposals
MLSP is seeking original, high quality proposals for Special Sessions, to be included in the technical program along with the regular track. Special Sessions are expected to address research in focused, emerging, or interdisciplinary areas of particular interest, not covered already by traditional MLSP sessions.
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