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CVPR
2012
IEEE
13 years 6 months ago
Geometry constrained sparse coding for single image super-resolution
The choice of the over-complete dictionary that sparsely represents data is of prime importance for sparse codingbased image super-resolution. Sparse coding is a typical unsupervi...
Xiaoqiang Lu, Haoliang Yuan, Pingkun Yan, Yuan Yua...
ACL
2012
13 years 6 months ago
Improving Word Representations via Global Context and Multiple Word Prototypes
Unsupervised word representations are very useful in NLP tasks both as inputs to learning algorithms and as extra word features in NLP systems. However, most of these models are b...
Eric H. Huang, Richard Socher, Christopher D. Mann...
SIMPRA
2008
83views more  SIMPRA 2008»
15 years 4 months ago
Learning by doing, learning spillovers and the diffusion of fuel cell vehicles
Fuel cell vehicles (FCVs) running on hydrogen do not cause local air pollution. Depending on the energy sources used to produce the hydrogen they may also reduce greenhouse gases ...
Malte Schwoon
100
Voted
TSMC
2002
69views more  TSMC 2002»
15 years 3 months ago
A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environm
An extended algorithm of the relative reward strength algorithm is proposed. It is shown that the proposed algorithm ensures the convergence with probability 1 to the optimal path ...
Norio Baba, Yoshio Mogami
AI
2006
Springer
15 years 4 months ago
Robot introspection through learned hidden Markov models
In this paper we describe a machine learning approach for acquiring a model of a robot behaviour from raw sensor data. We are interested in automating the acquisition of behaviour...
Maria Fox, Malik Ghallab, Guillaume Infantes, Dere...