[Talks@IDSIA] Fwd: A. Bronstein - Learning efficient parsimonious models

Resent-From: <jonathan(a)idsia.ch&gt; From: Announcements of talks at IDSIA <talks(a)idsia.ch&gt; Subject: [Talks@IDSIA] A. Bronstein - Learning efficient parsimonious models Date: September 1, 2012 3:59:10 PM GMT+02:00 To: talks(a)idsia.ch Reply-To: talks(a)idsia.ch Dear colleagues, I'm pleased to announce a talk by Alex Bronstein. WHEN: Wed., Sep. 19th; 11:30-12:00 WHERE: Room 222, Galleria 1 TITLE: Learning efficient parsimonious models SPEAKER: Alex Bronstein, Tel Aviv University ABSTRACT: Parsimony, preferring a simple explanation to a more complex one, is probably one of the most intuitive heuristic principles widely adopted in the modeling of nature. The past two decades of research have shown the power of parsimonious representation in a vast variety of applications from diverse domains of science, especially in signal and image processing, computer vision, and machine learning. A common form of parsimony is sparsity, postulating that data can be represented by a small number of non-zero coefficients in an appropriate dictionary. This model is satisfied by many classes of natural signals and can be efficiently pursued through convex, greedy, and other optimization methods. Other manifestations of parsimony successfully capturing more intricate data structures are various forms of structured sparsity and low rank matrices and tensors. Existing parsimonious modelling approaches are model-centric, following the same pattern: First, an objective comprising a fitting term and parsimony-promoting penalty terms is constructed; next, an iterative optimization algorithm is applied to minimize the objective, pursuing either the parsimonious representation of the data in a given dictionary, or the dictionary itself. Despite the steady improvement of iterative optimization tools, their inherently sequential structure and data-dependent complexity and latency constitute a major limitation in many applications requiring real-time performance or involving large-scale data. Also, representations obtained via optimization are hard to incorporate into a higher-level optimization problem, practically restricting existing parsimonious models to unsupervised regimes. Consequently, these models are typically generative rather than discriminative, rendering difficult several important applications such as similarity learning for large-scale information retrieval. We present a novel approach, in which the emphasis is moved from the model to the pursuit algorithm, and develop a process-centric view of parsimonious modeling. We show a family of fixed-complexity processes derived from proximal descent algorithms and tuned by learning, capable of outperforming traditional parsimonious modeling approaches based on optimization. We demonstrate several applications, in which the new framework achieves state-of-the-art result, including single-channel audio separation, denoising, and speaker identification, as well as background subtraction in video. BIO: Alex Bronstein received the B.Sc. and M.Sc. with honors from the Department of Electrical Engineering, and Ph.D. from the Department of Computer Science, Technion, and in currently with the School of Electrical Engineering at Tel Aviv University where he is the founder and head of the 3D Imaging and Vision laboratory, and the head of the Digital Signal Processing laboratory. His main research interests are theoretical and computational methods in computer vision, pattern recognition, shape analysis, and machine learning. He is a coauthor of the first book systematically treating computational analysis of deformable shapes, and chaired the first IEEE workshops on the topic. Alex Bronstein is the alumnus of the Technion Excellence Program and the Academy of Achievement. His research was recognized by numerous awards, including the Hershel Rich Technion Innovation award, the Gensler counter-terrorism prize, the Adams Fellowship, and the Krill prize by the Wolf Foundation. Dr. Bronstein held visiting appointments in several universities including Stanford. In addition to his academic activities, he was a co-founder of the Silicon Valley startup Novafora, where he served as vice president of video technology, and one of the principal technologists and inventors behind the 3D acquisition technology developed by the Israeli startup Invision and acquired by Intel in 2012. ============= Jonathan Masci _______________________________________________ Talks mailing list Talks(a)idsia.ch http://mailman2.ti-edu.ch/mailman/listinfo/talks