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NIPS
2007
13 years 11 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
KDD
2004
ACM
330views Data Mining» more  KDD 2004»
14 years 10 months ago
Learning to detect malicious executables in the wild
In this paper, we describe the development of a fielded application for detecting malicious executables in the wild. We gathered 1971 benign and 1651 malicious executables and enc...
Jeremy Z. Kolter, Marcus A. Maloof
DNIS
2005
Springer
102views Database» more  DNIS 2005»
14 years 3 months ago
An Improved Approach to Extract Document Summaries Based on Popularity
With the rapid growth of the Internet, most of the textual data in the form of newspapers, magazines and journals tend to be available on-line. Summarizing these texts can aid the...
P. Arun Kumar, K. Praveen Kumar, T. Someswara Rao,...
ICPR
2008
IEEE
14 years 4 months ago
Model-based visual self-localization using geometry and graphs
In this paper, a geometric approach for global selflocalization based on a world-model and active stereo vision is introduced. The method uses class specific object recognition a...
David Israel Gonzalez-Aguirre, Tamim Asfour, Eduar...
CVPR
2009
IEEE
14 years 4 months ago
Recognizing realistic actions from videos
In this paper, we present a systematic framework for recognizing realistic actions from videos “in the wild.” Such unconstrained videos are abundant in personal collections as...
Jingen Liu, Jiebo Luo, Mubarak Shah