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121
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JMLR
2010
147views more  JMLR 2010»
14 years 7 months ago
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
Rahul Mazumder, Trevor Hastie, Robert Tibshirani
124
Voted
CORR
2011
Springer
193views Education» more  CORR 2011»
14 years 4 months ago
Gigapixel Binary Sensing: Image Acquisition Using Oversampled One-Bit Poisson Statistics
We study a new gigapixel image sensor that is reminiscent of traditional photographic film. Each pixel in the sensor has a binary response, giving only a one-bit quantized measur...
Feng Yang, Yue M. Lu, Luciano Sbaiz, Martin Vetter...
93
Voted
ICASSP
2011
IEEE
14 years 4 months ago
A Lagrangian dual relaxation approach to ML MIMO detection: Reinterpreting regularized lattice decoding
This paper describes a new approximate maximum-likelihood (ML) MIMO detection approach by studying a Lagrangian dual relaxation (LDR) of ML. Unlike many existing relaxed ML method...
Jiaxian Pan, Wing-Kin Ma
128
Voted
ICCV
2011
IEEE
14 years 15 days ago
Generalized Subgraph Preconditioners for Large-Scale Bundle Adjustment
We present a generalized subgraph preconditioning (GSP) technique to solve large-scale bundle adjustment problems efficiently. In contrast with previous work which uses either di...
Yong-Dian Jian, Doru C. Balcan, Frank Dellaert
118
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ECCV
2008
Springer
16 years 2 months ago
Efficiently Learning Random Fields for Stereo Vision with Sparse Message Passing
As richer models for stereo vision are constructed, there is a growing interest in learning model parameters. To estimate parameters in Markov Random Field (MRF) based stereo formu...
Jerod J. Weinman, Lam Tran, Christopher J. Pal