'Spectral methods' diaporamas de présentation

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Spectral Differencing with a Twist

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On the robustness of dictatorships: spectral methods.

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CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View of Spectral Seg

Markus Herrgard UCSD Bioengineering and Bioinformatics. CSE 291 Fall 2001 Marina Meila and Jianbo Shi: Learning Segmentation by Random Walks/A Random Walks View of Spectral Segmentation. Overview. Introduction: Why random walks? Review of the Ncut algorithm Finite Markov chains

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Sparsity and Saliency

Sparsity and Saliency. for the Crash Course on Visual Saliency Modeling: Behavioral Findings and Computational Models CVPR 2013. Xiaodi Hou K-Lab, Computation and Neural Systems California Institute of Technology. Schedule. A brief history of. Spectral Saliency Detection.

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Lecture 17 Introduction to Eigenvalue Problems

Lecture 17 Introduction to Eigenvalue Problems. Shang-Hua Teng. Eigenvalue Problems. Eigenvalue problems occur in many areas of science and engineering E.g., Structure analysis It is important for analyzing numerical and linear algebra algorithms

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The Finite Element Method Defined

The Finite Element Method Defined. The Finite Element Method (FEM) is a weighted residual method that uses compactly-supported basis functions. Brief Comparison with Other Methods. Finite Difference (FD) Method:

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Graph clustering

Graph clustering. Jin Chen CSE891- 001 Fall 2012 MSU. Graph clustering. Spectral methods that target weighted cuts form an important class of such algorithms and are shown to be very effective for problems such as image segmentation Multi -level graph partitioning algorithms

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Non-MHD Models

Non-MHD Models. Wendy Mata University of California, Los Angeles GEM Summer Workshop Student tutorial June 16, 2013. Simulations are an important tool in scientific research. Computer simulations are carried out to: Understand the consequences of fundamental physical laws

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Topologically biased random walks with application for community finding

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Modeling Geophysical Fluid Flows

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Lecture 6 CMS 165

Lecture 6 CMS 165. Spectral Methods: Tensor methods. Recap: PCA on Gaussian Mixtures. Can obtain span(A). But what about columns of A?. Learning Gaussian mixtures through clustering. Hidden Markov Models. Source: slides from Daniel Hsu. Discrete Hidden Markov Models.

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Welcome to the Kernel-Class

Welcome to the Kernel-Class. My name : Max (Welling) Book: There will be class-notes/slides. Homework : reading material, some exercises, some MATLAB implementations. I like : an active attitude in class. ask questions! respond to my questions. Enjoy.

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