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» Learning to learn with the informative vector machine
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ACSC
2008
IEEE
13 years 11 months ago
An investigation of the state formation and transition limitations for prediction problems in recurrent neural networks
Recurrent neural networks are able to store information about previous as well as current inputs. This "memory" allows them to solve temporal problems such as language r...
Angel Kennedy, Cara MacNish
CVPR
2011
IEEE
13 years 26 days ago
Discriminative Spatial Pyramid
Spatial Pyramid Representation (SPR) is a widely used method for embedding both global and local spatial information into a feature, and it shows good performance in terms of gene...
Tatsuya Harada, Yoshitaka Ushiku, Yuya Yamashita, ...
CVPR
2005
IEEE
14 years 11 months ago
Tangent-Corrected Embedding
Images and other high-dimensional data can frequently be characterized by a low dimensional manifold (e.g. one that corresponds to the degrees of freedom of the camera). Recently,...
Ali Ghodsi, Jiayuan Huang, Finnegan Southey, Dale ...
MM
2010
ACM
219views Multimedia» more  MM 2010»
13 years 9 months ago
A framework for photo-quality assessment and enhancement based on visual aesthetics
We present an interactive application that enables users to improve the visual aesthetics of their digital photographs using spatial recomposition. Unlike earlier work that focuse...
Subhabrata Bhattacharya, Rahul Sukthankar, Mubarak...
CIKM
2010
Springer
13 years 6 months ago
Regularization and feature selection for networked features
In the standard formalization of supervised learning problems, a datum is represented as a vector of features without prior knowledge about relationships among features. However, ...
Hongliang Fei, Brian Quanz, Jun Huan