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169
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JMLR
2012
13 years 5 months ago
Online Incremental Feature Learning with Denoising Autoencoders
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
Guanyu Zhou, Kihyuk Sohn, Honglak Lee
PPOPP
2003
ACM
15 years 7 months ago
Improving server software support for simultaneous multithreaded processors
Simultaneous multithreading (SMT) represents a fundamental shift in processor capability. SMT's ability to execute multiple threads simultaneously within a single CPU offers ...
Luke McDowell, Susan J. Eggers, Steven D. Gribble
WACV
2005
IEEE
15 years 8 months ago
Incorporating Background Invariance into Feature-Based Object Recognition
Current feature-based object recognition methods use information derived from local image patches. For robustness, features are engineered for invariance to various transformation...
Andrew N. Stein, Martial Hebert
138
Voted
IDEAL
2004
Springer
15 years 8 months ago
Prediction of Implicit Protein-Protein Interaction by Optimal Associative Feature Mining
Proteins are known to perform a biological function by interacting with other proteins or compounds. Since protein–protein interaction is intrinsic to most cellular processes, pr...
Jae-Hong Eom, Jeong Ho Chang, Byoung-Tak Zhang
113
Voted
ICPR
2006
IEEE
16 years 3 months ago
Background Robust Object Labeling by Voting of Weight-Aggregated Local Features
In this paper, we present a new voting-based object labeling method that is robust to background clutter. The conventional simple voting method shows very poor performance under c...
In-So Kweon, Kuk-Jin Yoon, Sungho Kim