Title

Deep Learning: history, models & challenges, with an application in signal processing and mobile authentification

Abstract

Representation Learning (also known with its more popular title « Deep Learning ») consists in automatically learning layered and hierarchical representation with various layers abstraction from large amounts of data. This presentation will review the history of the field, the main actors and the major scientific challenges. We will first present a brief introduction into common deep models like convolutional neural networks and recurrent networks, before going more into detail of some selected applications in signal processing.

In particular, we present a large-scale study, exploring the capability of temporal deep neural networks in interpreting natural human kinematics and introduce the first method for active biometric authentication with mobile inertial sensors. This work has been done in collaboration with Google, where the first-of-its-kind dataset of human movements has been passively collected by 1500 volunteers using their smartphones daily over several months. We propose an optimized shift-invariant dense convolutional mechanism (DCWRNN) and incorporate the discriminatively-trained dynamic features in a probabilistic generative framework taking into account temporal characteristics. Our results demonstrate, that human kinematics convey important information about user identity and can serve as a valuable component of multi-modal authentication systems.

Biography

Christian WOLF is associate professor (Maitre de Conférences, HDR) at INSA de Lyon and LIRIS UMR 5205, a CNRS laboratory, since 2005. He is interested in computer vision and machine learning, deep learning, especially in the visual analysis of complex scenes in motion: gesture and activity recognition and pose estimation. In his work he puts an emphasis on models of complex interactions, on structured models, graphical models and on deep learning. He received his MSc in computer science from Vienna University of Technology (TU Wien) in 2000, and a PhD in computer science from INSA de Lyon, France, in 2003. In 2012 he obtained the habilitation diploma, also from INSA de Lyon. Since September 2017 Christian is on leave at INRIA, at the chroma work group at the CITI laboratory (“délégation INRIA”).

Abstract
Ultrascale architectures involve large-scale complex systems joining parallel and distributed computing infrastructures joining technology trends (i.e. RISC/ CISIC/ASICS systems) ultrascale software and hybrid models (i.e. cloud/Edge and Fog)  that will be extended in different escenarios. Precisely, this complexity propose special challenges from different points of view: sustainability, scalability, dynamicity, energy-aware, usability, data management, dependability  and more, in a software and hardware relationship. This presentation shows some towards about this ultrscale architectures, observing the different challenges and how it is possible to threat some of them, depending the context a point of view software/hardware and application context.

Bio
Professor Carlos J. Barrios H. is Doctor in Computer Science of the Nice–Côte d’Azur University in France. Researcher in High Performance Computing and Large Scale Architectures, he works in projects associated with hybrid/high performance architectures for science and ultrascale systems involving design, performance evaluation and implementation mechanisms. From 2012, Professor Barrios is the director of the High Performance and Scientific Computing Center of the Universidad Industrial de Santander in Colombia and assistant professor of the same university. At same time, he is the general chair of the Advanced Computing Services for Latin America and Caribbean and he’s involved in different HPC collaboration networks between Europe and Latin America. Contact: cbarrios@uis.edu.co