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JMLR
2010
147views more  JMLR 2010»
14 years 4 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
CORR
2011
Springer
193views Education» more  CORR 2011»
14 years 1 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...
ICASSP
2011
IEEE
14 years 1 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
ICCV
2011
IEEE
13 years 10 months 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
ECCV
2008
Springer
15 years 12 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