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BC
2002
193views more  BC 2002»
13 years 6 months ago
Resonant spatiotemporal learning in large random recurrent networks
Taking a global analogy with the structure of perceptual biological systems, we present a system composed of two layers of real-valued sigmoidal neurons. The primary layer receives...
Emmanuel Daucé, Mathias Quoy, Bernard Doyon
ICML
2005
IEEE
14 years 7 months ago
Learning first-order probabilistic models with combining rules
Many real-world domains exhibit rich relational structure and stochasticity and motivate the development of models that combine predicate logic with probabilities. These models de...
Sriraam Natarajan, Prasad Tadepalli, Eric Altendor...
COLING
2002
13 years 6 months ago
A Comparative Evaluation of Data-driven Models in Translation Selection of Machine Translation
We present a comparative evaluation of two data-driven models used in translation selection of English-Korean machine translation. Latent semantic analysis(LSA) and probabilistic ...
Yuseop Kim, Jeong Ho Chang, Byoung-Tak Zhang
EXACT
2009
13 years 4 months ago
Towards the Explanation of Workflows
Across many fields involving complex computing, software systems are being augmented with workflow logging functionality. The log data can be effectively organized using declarativ...
James Michaelis, Li Ding, Deborah L. McGuinness
CORR
2011
Springer
174views Education» more  CORR 2011»
12 years 10 months ago
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a ...
Yoshitaka Kameya, Taisuke Sato