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82
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IJCAI
1989
15 years 1 months ago
Generation, Local Receptive Fields and Global Convergence Improve Perceptual Learning in Connectionist Networks
This paper presents and compares results for three types of connectionist networks on perceptual learning tasks: [A] Multi-layered converging networks of neuron-like units, with e...
Vasant Honavar, Leonard Uhr
PAMI
2008
119views more  PAMI 2008»
15 years 21 days ago
Triplet Markov Fields for the Classification of Complex Structure Data
We address the issue of classifying complex data. We focus on three main sources of complexity, namely, the high dimensionality of the observed data, the dependencies between these...
Juliette Blanchet, Florence Forbes
CVPR
2000
IEEE
16 years 2 months ago
Learning in Gibbsian Fields: How Accurate and How Fast Can It Be?
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
Song Chun Zhu, Xiuwen Liu
DAS
2008
Springer
15 years 2 months ago
Super-Resolution of Text Images Using Edge-Directed Tangent Field
This paper presents an edge-directed super-resolution algorithm for gray level document images without using any training set. This technique creates an image with smooth regions ...
Jyotirmoy Banerjee, C. V. Jawahar
97
Voted
NIPS
2001
15 years 2 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr