DL 2017 - 2017 IEEE Symposium on Deep Learning (IEEE DL'17)

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.
Topics
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
Accepted Special Sessions
Akihito Sudo, Tokyo University, Japan
Aldy Gunawan, Singapore Management Univ, Singapore
Alessandro Sperduti, University of Padova, Italy
Amit Sethi, Indian Inst.of Technology Guwahati, India
Arijit Sur, Indian Inst.of Technology Guwahati, India
Ashish Anand, Indian Inst. Technology Guwahati, India
Chrisina Jayne, Robert Gordon University, UK
Dmitry Kangin, Exeter University, UK
Erdal Kayacan, Nanyang Technological Univ., Singapore
Feng Yuhong, Shenzhen University, China
Huang Guang Bin, Nanyang Technological Univ, Singapore
Jose C. Principe, University of Florida, US
Justin Dauwels, Nanyang Technological Univ., Singapore
Olga Senyukova, Lomonosov Moscow State Univ., Russia
Teck-Hou Teng, Singapore Management Univ., Singapore
Wang Lipo, Nanyang Technological Univ., Singapore
Wang Di, Nanyang Technological Univ., Singapore
William Howell, Natural Resources Canada