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Deep Learning (DL) is growing in popularity because it exploits rather well the unreasonable effectiveness of data to solve complex problems in machine learning. In fact, 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. Sponsored by the IEEE Computational Intelligence Society, this event will attract top scientists, researchers, professionals, practitioners and students from around the world.

The goal of the IEEE Symposium on DL is to provide a forum for interactions between researchers and practitioners in DL as well as in Artificial Neural Networks, Bayesian Learning, Generative and Predictive Modeling, Optimization, Cognitive Architectures and Machine Learning with an interest in DL. We are interested in discussing the new DL advances, the challenges ahead, and to brainstorm about new solutions and directions. We also seek applications from large engineering firms dedicated to construction and services in energy, autonomous transportation, communications industries, web, marketing, medical and financial services, and scientific fields that require big data analytics.
Topics of IEEE DL’17 include but are not limited to:

• Unsupervised, semi-supervised, and supervised learning
• Deep reinforcement learning (deep value function estimation, policy learning and stochastic control)
• Memory Networks and differentiable programming
• Implementation issues, both software and hardware platforms
• Applications in vision, audio, speech, natural language processing, robotics, navigation, control, games AI, cognitive architectures, etc.
• 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
• Paralleliisation in DL
• Non-Iterative DL
• Recursive DL
• Incremental DL
• Evolving DL
• Fast DL

Alessandro Sperdutii
Università di Padova, Italy
Email: sperduti@math.unipd.it


Jose Principe
University of Florida, USA
Email: principe@cnel.ufl.edu


Plamen Angelov
University of Lancaster, UK
Email: p.angelov@lancaster.ac.uk