Abstract: In the past years, deep learning methods have achieved unprecedented performance on a broad range of problems in various fields from computer vision to speech recognition. So far research has mainly focused on developing deep learning methods for grid-structured data, while many important applications have to deal with graph-structured data. Such geometric data are becoming increasingly important in computer graphics and 3D vision, sensor networks, drug design, biomedicine, recommendation systems, NLP and computer vision with knowledge graphs, and web applications. The purpose of this talk is to introduce convolutional neural networks architectures on graphs, as well as applications for this class of problems.
Abstract: Many convex optimization problems arising from the context of machine learning and signal processing satisfies the so-called local quadratic growth condition, which can be understood as a generalization of strong convexity. When solving such problems, classical (non-accelerated) gradient and coordinate descent methods automatically have a linear rate of convergence, whereas one needs to know explicitly the strong convexity (or error bound) parameter in order to set accelerated gradient and accelerated coordinate descent methods to have the optimal linear rate of convergence. Setting the algorithm with an incorrect parameter may result in a slower algorithm, sometimes even slower than if we had not tried to set an acceleration scheme.
We show that restarting accelerated proximal gradient algorithms at any frequency gives a globally linearly convergent algorithm. Then, as the rate of convergence depends on the match between the frequency and the quadratic error bound, we design a scheme to automatically adapt the frequency of restart from the observed decrease of the norm of the gradient mapping. Restarting accelerated coordinate descent type methods, as well as accelerated stochastic variance reduced methods, also leads to a geometric rate of convergence, under the more restrictive assumption that the objective function is strongly convex. We propose a well chosen sequence of restarting times to achieve a nearly optimal linear convergence rate, without knowing the actual value of the error bound.
Abstract: The Machine Learning Hub @ KAUST is designed to be the one-stop-shop for machine learning (ML) and artificial intelligence (AI) at KAUST. It is an informal forum for exchanging ideas in these areas, including (but not limited to) theoretical foundations, systems, tools, and applications. It will be providing several offerings to the KAUST community interested in ML and AI, including a regular seminar series where new research in the field is presented, an online social forum dedicated to AI and ML discussions, announcements, brainstorming, and collaborations, and hands-on activities (e.g. tutorials/workshops and hackathons) to bolster the growing need for ML and AI education/training on campus. For more details, please visit ml.kaust.edu.sa. This talk will serve as the first installment in the seminar series, during which a more detailed overview of The Hub will be presented. All who are interested in ML and AI on campus are invited to join.