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JMLR
2012
11 years 11 months ago
Metric and Kernel Learning Using a Linear Transformation
Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over low-dimensional d...
Prateek Jain, Brian Kulis, Jason V. Davis, Inderji...
ECCV
2008
Springer
14 years 10 months ago
Learning for Optical Flow Using Stochastic Optimization
Abstract. We present a technique for learning the parameters of a continuousstate Markov random field (MRF) model of optical flow, by minimizing the training loss for a set of grou...
Yunpeng Li, Daniel P. Huttenlocher
KDD
2010
ACM
272views Data Mining» more  KDD 2010»
13 years 6 months ago
Scalable similarity search with optimized kernel hashing
Scalable similarity search is the core of many large scale learning or data mining applications. Recently, many research results demonstrate that one promising approach is creatin...
Junfeng He, Wei Liu, Shih-Fu Chang
DAC
2006
ACM
14 years 9 months ago
Optimal cell flipping in placement and floorplanning
In a placed circuit, there are a lot of movable cells that can be flipped to further reduce the total wirelength, without affecting the original placement solution. We aim at solv...
Chiu-Wing Sham, Evangeline F. Y. Young, Chris C. N...
EOR
2010
149views more  EOR 2010»
13 years 8 months ago
Adaptive multicut aggregation for two-stage stochastic linear programs with recourse
Outer linearization methods for two-stage stochastic linear programs with recourse, such as the L-shaped algorithm, generally apply a single optimality cut on the nonlinear object...
Svyatoslav Trukhanov, Lewis Ntaimo, Andrew Schaefe...