Special Session on Deep Learning for Brain-Like Computing and Pattern Recognition @ IJCNN 2016.

Description: CDeep learning is a topic of broad interest, both to researchers who develop new deep architectures and learning algorithms, as well as to practitioners who apply deep learning models to a wide range of applications, from image classification to video tracking, etc. Brain-like computing combines computational techniques with cognitive ideas, principles and models inspired by the brain for building information systems used in humans’ common life. Pattern recognition is a conventional area of artificial intelligence, which focuses on the recognition of patterns and regularities in data.

Recently, there has been very rapid and impressive progress in these three areas, in terms of both theories and applications, but many challenges remain. This workshop aims at bringing together researchers in machine learning and related areas to discuss the utility of deep learning for brain-like computing and pattern recognition, the advances, the challenges we face, and to brainstorm about new solutions and directions.

Topics of interest to the special session include, but are not limited to:

• unsupervised, semisupervised, and supervised deep learning;
• active learning, transfer learning and multi-task learning;
• dimensionality reduction, metric learning and kernel learning;
• sparse modeling;
• ensemble learning;
• hierarchical architectures;
• Doptimization for deep models;
• intelligent data analysis and recommendation systems;
• implementation issues, parallelization, software platforms, hardware for deep learning and big data analysis
• applications in video, image, texture, text processing, neuroscience, medical imaging or any other field.

Special Session Web Site