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. The IEEE DL'16 Symposium will be held simultaneously with other symposia and workshops in one location at the 2016 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2016) in Greece. Sponsored by the IEEE Computational Intelligence Society, this event will attract top scientists, researchers, professionals, practitioners and students from around the world. The registration to SSCI 2016 will allow participants to attend all the symposia, including the complete set of the proceedings of all the meetings, coffee breaks, lunches, and the banquet.
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.
IEEE DL’16 solicits papers that report new theoretical contributions, methodologies, algorithms and research results that apply DL technologies to the fields specified above, or other relevant applications. Topics of IEEE DL’16 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
• Multi-task learning • Learning from multiple modalities
• Weakly supervised learning • Metric learning and kernel learning
• Dimensionality expansion and sparse modeling
• Learning representations from large-scale data
• Hierarchical models
• Implementation issues, both software and hardware platforms
• Applications in vision, audio, speech, natural language processing, robotics, navigation, control, games AI, cognitive architectures, etc.
All special session proposals containing a title of the special session, to which symposium it is proposed to, names, affiliations and email addresses of proposers, together with a description of aims and scope of the special session and a list of 6-10 potential contributors, should be sent to email@example.com.
Further information about special session proposals and a list of accepted special sessions can be found at:http://ssci2016.cs.surrey.ac.uk/Special%20Sessions.htm
Università di Padova, Italy
|Xue-wen (William) Chen
Wayne State University, US
Jose C. Principe
University of Florida, US