2019 IEEE Symposium Series on Computational Intelligence


IEEE Symposium Series on Computational Intelligence

December 6-9, 2019 Xiamen, China

 

IEEE Symposium on Deep Learning (IEEE DL)

Deep Learning (DL) is growing in popularity because it solves complex problems in machine learning by exploiting multi scale, multi-layer architectures making better use of the data patterns. Multi-scale machine perception tasks such as object and speech recognitions using DL have recently outperformed systems that have been under development for many years. The principles of DL, and its ability to capture multi scale representations, are very general and the technology can be applied to many other problem domains, which makes it quite attractive. Many open problems and challenges still exists, e.g. interpretability, computational and time costs, repeatability of the results, convergence, ability to learn from a very small amount of data, to evolve dynamically/continue to learn, etc. The Symposium will provide a forum for discussing new DL advances, challenges, brainstorming new solutions and directions between top scientists, researchers, professionals, practitioners and students with an interest in DL and related areas including applications to autonomous transportation, communications, medical, financial services, etc.

Topics

Topics of IEEE DL’19 include but are not limited to:

  • Unsupervised, semi-, and supervised learning
  • Deep reinforcement learning (deep value function estimation, policy learning and stochastic control)
  • Memory Networks and differentiable programming
  • Implementation issues (software and hardware)
  • Dimensionality expansion and sparse modeling
  • Learning representations from large-scale data
  • Multi-task learning
  • Learning from multiple modalities
  • Weakly supervised learning
  • Metric learning and kernel learning
  • Hierarchical models
  • Interpretable DL
  • Fuzzy rule-based DL
  • Non-Iterative DL
  • Recursive DL
  • Repeatability of results in DL
  • Convergence in DL
  • Incremental DL
  • Evolving DL
  • Fast DL
  • Applications in:

    • Image/video
    • Audio/speech
    • Natural language processing
    • Robotics, navigation, control
    • Games
    • Cognitive architectures
    • AI

Symposium Chairs

Alessandro Sperdutii

Università di Padova, Italy

Email: sperduti@math.unipd.it

Homepage

Jose Principe

University of Florida, USA

Email: principe@cnel.ufl.edu

Homepage

Plamen Angelov

University of Lancaster, UK

Email: p.angelov@lancaster.ac.uk

Homepage

Program Committee

Plamen Angelov Lancaster University, UK
Chrisina Jayne Robert Gordon University, UK
Xiaowei Gu Lancaster University, UK
Dmitry Kangin Exeter University, UK
William Howell Natural Resources, Canada
Jose C. Principe University of Florida, US
Manuel Roveri Polytecnico di Milano, Italy
Olga Senyukova Lomonosov Moscow State Univ., Russia
Alessandro Sperduti University of Padova, Italy
Akihito Sudo Tokyo University, Japan
Teck-Hou Teng Singapore Management Univ., Singapore
Feng Yuhong Shenzhen University, China
Barbara Hammer Bielefeld University, Germany
Davide Bacciu Pisa University, Italy
Nicolò Navarin Padova University, Italy