Special Session on Theoretical Foundations of Deep Learning Models and Algorithms @ IJCNN 2016.

Description: Deep learning models and techniques are becoming more and more the computational tool of choice when facing difficult applicative problems, such as speech and image understanding. The reason for this huge interest in deep learning is due to the fact that their adoption leads to human (and, in some cases, super-human) performances. These successes, however, have been mainly obtained on empirical basis, often thanks to the computational power provided by parallel computer facilities such as GPUs or CPU clusters.

Although some recent works have addressed deep learning from a theoretical perspective, still there is a limited understanding of why deep architectures work so well and on how to design computationally efficient and effective training This special session aims to gather together leading scientists in deep learning and related areas within computational intelligence, neuroscience, machine learning, artificial intelligence, mathematics, and statistics, interested in all aspects of deep architectures and deep learning, with a particular emphasis on understanding fundamental principles.

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

• Theoretical results on representation and learning in natural or artificial deep architectures;
• Theoretical and/or empirical analysis of specific natural or artificial deep architectures or algorithms;
• Innovative deep architectures/algorithms for data representation and analysis, including both supervised methods like deep convolution networks and unsupervised ones like stacked auto-encoders and deep Boltzmann machines;
• Design and/or analysis of recurrent and recursive architectures for processing of sequences and more general data structures;
• Applications of deep learning in data representation and analysis, including recognition, understanding, detection, segmentation, retrieval, restoration, super-resolution, and compression;
• Deep learning algorithms that efficiently handle large-scale data.

Alessandro Sperduti, Univ. Padova (Italy), Jose C. Principe, University of Florida (USA), Plamen Angelov, Lancaster University (UK).
Special Session Web Site