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» A New Discriminative Kernel From Probabilistic Models
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ICML
2008
IEEE
16 years 20 days ago
Extracting and composing robust features with denoising autoencoders
Previous work has shown that the difficulties in learning deep generative or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to use...
Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pi...
CVIU
2011
14 years 3 months ago
Single and sparse view 3D reconstruction by learning shape priors
In this paper, we aim to reconstruct free-form 3D models from only one or few silhouettes by learning the prior knowledge of a specific class of objects. Instead of heuristically...
Yu Chen, Roberto Cipolla
CCS
2005
ACM
15 years 5 months ago
On deriving unknown vulnerabilities from zero-day polymorphic and metamorphic worm exploits
Vulnerabilities that allow worms to hijack the control flow of each host that they spread to are typically discovered months before the worm outbreak, but are also typically disc...
Jedidiah R. Crandall, Zhendong Su, Shyhtsun Felix ...
ICDM
2009
IEEE
188views Data Mining» more  ICDM 2009»
14 years 9 months ago
Binomial Matrix Factorization for Discrete Collaborative Filtering
Matrix factorization (MF) models have proved efficient and well scalable for collaborative filtering (CF) problems. Many researchers also present the probabilistic interpretation o...
Jinlong Wu
BMCBI
2007
138views more  BMCBI 2007»
14 years 12 months ago
A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic
Background: A key challenge in metabonomics is to uncover quantitative associations between multidimensional spectroscopic data and biochemical measures used for disease risk asse...
Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soin...