32nd IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

Prospective authors are invited to submit papers on relevant algorithms and applications including, but not limited to:

• Cognitive information learning
• Deep learning techniques
• Dictionary learning
• Graphical and kernel methods
• Matrix factorization/completion
• Independent component analysis
• Information-theoretic learning
• Learning theory and algorithms
• Learning form multimodal data

• ML over wireless networks
• Applications in music and audio
• Pattern recognition and classification
• Subspace and manifold learning
• Sequential learning
• Distributed/Federated learning
• Reinforcement learning
• Transfer learning
• Self/semi-supervised learning

Submission of papers: Prospective authors are invited to submit 6 pages full-length papers, including figures and references. All accepted and presented papers will be published in and indexed by IEEE Xplore.