'Low rank' diaporamas de présentation

Learning Non-Parametric Basis Independent Models from Point-Queries via Low-rank Methods

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Learning User Preferences

Learning User Preferences. Jason Rennie MIT CSAIL jrennie@gmail.com. Advisor: Tommi Jaakkola. Information Extraction. Informal Communication: e-mail, mailing lists, bulletin boards Issues: Context switching Abbreviations & shortened forms Variable punctuation, formatting, grammar.

583 views • 45 slides

A Rank-Revealing Method for Low Rank Matrices with Updating, Downdating, and Applications

A Rank-Revealing Method for Low Rank Matrices with Updating, Downdating, and Applications. Tsung-Lin Lee (Michigan State University). joint work with Tien-Yien Li and Zhonggang Zeng. 2007 AMS Session Meeting, Chicago. Rank determination problems appear in 1. Image Processing

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Outline

Ensemble b ased Data Assimilation Massimo Bonavita massimo.bonavita@ecmwf.int Contributions from: Lars Isaksen, Elias Holm, Mike Fisher, Laure Raynaud, A. Clayton, D. Barker. Outline. Variational vs Ensemble methods Hybrid methods The Ensemble of Data Assimilations (EDA) method

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Computationally Efficient Wavefront Reconstruction for Multi-Conjugate Adaptive Optics (MCAO)

Computationally Efficient Wavefront Reconstruction for Multi-Conjugate Adaptive Optics (MCAO). Brent Ellerbroek AURA New Initiatives Office IPAM Workshop on Estimation and Control Problems in Adaptive Optics January 23, 2004. Presentation Outline. Anisoplanatism and MCAO

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On the modeling and simulation of large-scale systems

On the modeling and simulation of large-scale systems. Venkataramanan (Ragu) Balakrishnan School of ECE, Purdue University Joint work with Stephen Cauley, Jitesh Jain, Hong Li, Cheng-Kok Koh (Purdue) and M. P. Anantram (NASA). Basic ideas. Many engineering system models are of a large scale

527 views • 31 slides

Mining Discriminative Components With Low-Rank and Sparsity Constraints for Face Recognition

Mining Discriminative Components With Low-Rank and Sparsity Constraints for Face Recognition. Qiang Zhang, Baoxin Li Computer Science and Engineering Arizona State University Tempe, AZ, 85281 qzhang53, baoxin.li@asu.edu. Problem Description.

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Recovering low rank and sparse matrices from compressive measurements

Recovering low rank and sparse matrices from compressive measurements. Aswin C Sankaranarayanan Rice University. Richard G. Baraniuk. Andrew E. Waters. Background subtraction in surveillance videos. s tatic camera with foreground objects. r ank 1 background. s parse foreground.

487 views • 26 slides

Exact Recovery of Low-Rank Plus Compressed Sparse Matrices

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Depth Enhancement via Low-rank Matrix Completion and Travel Report in Wien

Depth Enhancement via Low-rank Matrix Completion and Travel Report in Wien. Computer Vision Lab. School of Information and Communications GIST. Depth enhancement?. Low-rank matrix completion. Ground truth. RGB-D input. Enhanced depth map. With missing and noisy disparity values.

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Rank Minimization for Subspace Tracking from Incomplete Data

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Unveiling Anomalies in Large-scale Networks via Sparsity and Low Rank

Unveiling Anomalies in Large-scale Networks via Sparsity and Low Rank. Morteza Mardani, Gonzalo Mateos and Georgios Giannakis ECE Department, University of Minnesota Acknowledgments : NSF grants no. CCF-1016605, EECS-1002180. Asilomar Conference November 7, 2011. 1. Context.

298 views • 14 slides

Unveiling Anomalies in Large-scale Networks via Sparsity and Low Rank

Unveiling Anomalies in Large-scale Networks via Sparsity and Low Rank. Morteza Mardani, Gonzalo Mateos and Georgios Giannakis ECE Department, University of Minnesota Acknowledgments : NSF grants no. CCF-1016605, EECS-1002180. Asilomar Conference November 7, 2011. 1. Context.

204 views • 14 slides

Unveiling Anomalies in Large-scale Networks via Sparsity and Low Rank

Unveiling Anomalies in Large-scale Networks via Sparsity and Low Rank. Morteza Mardani, Gonzalo Mateos and Georgios Giannakis ECE Department, University of Minnesota Acknowledgments : NSF grants no. CCF-1016605, EECS-1002180. Asilomar Conference November 7, 2011. 1. Context.

204 views • 14 slides

Distributed Nuclear Norm Minimization for Matrix Completion

317 views • 12 slides