Y
Yair Weiss
Researcher at Hebrew University of Jerusalem
Publications - 136
Citations - 42595
Yair Weiss is an academic researcher from Hebrew University of Jerusalem. The author has contributed to research in topics: Belief propagation & Graphical model. The author has an hindex of 66, co-authored 133 publications receiving 39687 citations. Previous affiliations of Yair Weiss include University of California, Berkeley & Vassar College.
Papers
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Proceedings Article
On Spectral Clustering: Analysis and an algorithm
TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.
Proceedings Article
Spectral Hashing
TL;DR: The problem of finding a best code for a given dataset is closely related to the problem of graph partitioning and can be shown to be NP hard and a spectral method is obtained whose solutions are simply a subset of thresholded eigenvectors of the graph Laplacian.
Journal ArticleDOI
A Closed-Form Solution to Natural Image Matting
TL;DR: A closed-form solution to natural image matting that allows us to find the globally optimal alpha matte by solving a sparse linear system of equations and predicts the properties of the solution by analyzing the eigenvectors of a sparse matrix, closely related to matrices used in spectral image segmentation algorithms.
Journal ArticleDOI
Constructing free-energy approximations and generalized belief propagation algorithms
TL;DR: This work explains how to obtain region-based free energy approximations that improve the Bethe approximation, and corresponding generalized belief propagation (GBP) algorithms, and describes empirical results showing that GBP can significantly outperform BP.
Book
Understanding belief propagation and its generalizations
TL;DR: It is shown that BP can only converge to a fixed point that is also a stationary point of the Bethe approximation to the free energy, which enables connections to be made with variational approaches to approximate inference.