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- Andrew Y. Ng, Michael I. Jordan, Yair Weiss
- NIPS
- 2001

Yair Weiss School of CS & Engr. The Hebrew Univ. yweiss@cs.huji.ac.il Despite many empirical successes of spectral clustering methodsalgorithms that cluster points using eigenvectors of matrices derived from the datathere are several unresolved issues. First , there are a wide variety of algorithms that use the eigenvectors in slightly different ways.… (More)

- Yair Weiss, Antonio Torralba, Rob Fergus
- NIPS
- 2008

Semantic hashing[1] seeks compact binary codes of data-points so that the Hamming distance between codewords correlates with semantic similarity. In this paper, we show that 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. By relaxing the original problem, we… (More)

- Anat Levin, Dani Lischinski, Yair Weiss
- IEEE Transactions on Pattern Analysis and Machine…
- 2006

Interactive digital matting, the process of extracting a foreground object from an image based on limited user input, is an important task in image and video editing. From a computer vision perspective, this task is extremely challenging because it is massively ill-posed - at each pixel we must estimate the foreground and the background colors, as well as… (More)

- Jonathan S. Yedidia, William T. Freeman, Yair Weiss
- IEEE Transactions on Information Theory
- 2005

Important inference problems in statistical physics, computer vision, error-correcting coding theory, and artificial intelligence can all be reformulated as the computation of marginal probabilities on factor graphs. The belief propagation (BP) algorithm is an efficient way to solve these problems that is exact when the factor graph is a tree, but only… (More)

- Anat Levin, Dani Lischinski, Yair Weiss
- ACM Trans. Graph.
- 2004

Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious,… (More)

- Daniel Zoran, Yair Weiss
- 2011 International Conference on Computer Vision
- 2011

Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can lead to tremendous computational challenges. In contrast, when we work with small image patches, it is possible to learn priors and perform patch restoration very efficiently.… (More)

- Kevin P. Murphy, Yair Weiss, Michael I. Jordan
- UAI
- 1999

Recently, researchers have demonstrated that "loopy belief propagation" the use of Pearl's polytree algorithm in a Bayesian network with loops can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performance of "Turbo Codes" codes whose decoding algorithm is equivalent to loopy belief… (More)

- Anat Levin, Yair Weiss, Frédo Durand, William T. Freeman
- 2009 IEEE Conference on Computer Vision and…
- 2009

Blind deconvolution is the recovery of a sharp version of a blurred image when the blur kernel is unknown. Recent algorithms have afforded dramatic progress, yet many aspects of the problem remain challenging and hard to understand. The goal of this paper is to analyze and evaluate recent blind deconvolution algorithms both theoretically and experimentally.… (More)

- Jonathan S. Yedidia, William T. Freeman, Yair Weiss
- NIPS
- 2000

Belief propagation (BP) was only supposed to work for tree-like networks but works surprisingly well in many applications involving networks with loops, including turbo codes. However, there has been little understanding of the algorithm or the nature of the solutions it finds for general graphs. We show that BP can only converge to a stationary point of an… (More)

- Anat Levin, Yair Weiss, Frédo Durand, William T. Freeman
- CVPR
- 2011

In blind deconvolution one aims to estimate from an input blurred image y a sharp image x and an unknown blur kernel k. Recent research shows that a key to success is to consider the overall shape of the posterior distribution p(x, k|y) and not only its mode. This leads to a distinction between MAPx,k strategies which estimate the mode pair x, k and often… (More)