Resent-From: <jonathan(a)idsia.ch&gt; From: Jonathan Masci <jonathan(a)idsia.ch&gt; Subject: [IDSIA] Fwd: [Talks@IDSIA] A. Bronstein - Learning efficient parsimonious models Date: September 18, 2012 11:39:50 AM GMT+02:00 To: talks(a)idsia.ch, idsia(a)idsia.ch This is a reminder for the talk of A. Bronstein of tomorrow 19th at 11:30 in room 222. Begin forwarded message: > 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 _______________________________________________ Idsia mailing list Idsia(a)idsia.ch http://mailman.ti-edu.ch/mailman/listinfo/idsia