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» A discriminative model for semi-supervised learning
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MCS
2007
Springer
14 years 3 months ago
Group-Induced Vector Spaces
The strength of classifier combination lies either in a suitable averaging over multiple experts/sources or in a beneficial integration of complementary approaches. In this paper...
Manuele Bicego, Elzbieta Pekalska, Robert P. W. Du...
EMNLP
2007
13 years 10 months ago
Learning to Merge Word Senses
It has been widely observed that different NLP applications require different sense granularities in order to best exploit word sense distinctions, and that for many applications ...
Rion Snow, Sushant Prakash, Daniel Jurafsky, Andre...
JMLR
2012
11 years 11 months ago
On Nonparametric Guidance for Learning Autoencoder Representations
Unsupervised discovery of latent representations, in addition to being useful for density modeling, visualisation and exploratory data analysis, is also increasingly important for...
Jasper Snoek, Ryan Prescott Adams, Hugo Larochelle
ICCV
1999
IEEE
14 years 1 months ago
Learning Low-Level Vision
We describe a learning-based method for low-level vision problems--estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images, m...
William T. Freeman, Egon C. Pasztor
CVPR
2010
IEEE
14 years 5 months ago
Learning Mid-Level Features For Recognition
Many successful models for scene or object recognition transform low-level descriptors (such as Gabor filter responses, or SIFT descriptors) into richer representations of interme...
Y-Lan Boureau, Francis Bach, Yann LeCun, Jean Ponc...