Special Session on Deep Learning @ ESANN 2016.

Description: Deep learning models and techniques are nowadays the leading approaches to face complex machine learning and pattern recognition problems, especially when considering perceptual tasks such as speech and image understanding. The adoption of deep architectures, comprising multiple, adaptable, processing layers, has recently allowed significant improvements in performance for these type of tasks. Both unsupervised and supervised approaches for training deep architectures have been empirically explored, also thanks to the adoption of parallel computer facilities such as GPUs or CPU clusters. Despite of that, there is a limited understanding of why deep architectures work so well and on how to design computationally efficient and effective training algorithms. A natural source of inspiration for a better understanding of these issues is the study of human brain, where deep structures are now well recognized and pervasive (e.g. human visual recognition requires the activation of a hierarchy of processing stages and pathways.)

This special session focuses on all aspects of deep architectures and deep learning, with a particular emphasis on understanding fundamental principles. Because of that, it aims to bring together leading scientists in deep learning and related areas within neuroscience, machine learning, artificial intelligence, mathematics, and statistics.

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;
• Deep learning software and hardware architectures for applications.

Special Session on Deep Learning @ ESANN 2016