The 33rd MLSP Workshop is pleased to accept contributions on the most recent and novel research advances in the field of machine learning for signal processing that will be presented in single-track lecture and poster sessions.

Download PDF Call for Papers


Prospective authors are invited to submit papers on relevant theory and applications related, but not limited to, the following list of topics:

  • Deep learning techniques
  • Deep generative models
  • Self-supervised and semi-supervised learning
  • Adversarial machine learning
  • Graph neural networks
  • Learning from multimodal data
  • Learning theory and algorithms
  • Distributed/Federated learning
  • Machine learning over wireless networks
  • Reinforcement learning
  • Matrix factorization/completion
  • Dictionary learning
  • Source separation
  • Independent component analysis
  • Sparsity-aware learning
  • Tensor-based signal processing
  • Information-theoretic learning
  • Pattern recognition and classification
  • Feature extraction/selection/learning
  • Applications of machine learning

Papers must not be longer than 6 pages, including all text, figures and references, according to the Paper Submission Guidelines. All the accepted and presented papers will be published in and indexed by IEEE Xplore.

For inquiries about the technical program please contact:

Important Dates

  • April 28 May 10 (extended), 2023: Deadline for regular paper submissions
  • July 3, 2023: Notification of paper acceptance
  • July 17, 2023: Camera-ready upload