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» Learning Approximate Consistencies
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TNN
1998
111views more  TNN 1998»
14 years 11 months ago
Asymptotic distributions associated to Oja's learning equation for neural networks
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Jean Pierre Delmas, Jean-Francois Cardos
CP
2003
Springer
15 years 4 months ago
Approximated Consistency for Knapsack Constraints
Knapsack constraints are a key modeling structure in discrete optimization and form the core of many real-life problem formulations. Only recently, a cost-based filtering algorit...
Meinolf Sellmann
AAAI
2004
15 years 1 months ago
The Practice of Approximated Consistency for Knapsack Constraints
Knapsack constraints are a key modeling structure in discrete optimization and form the core of many real-life problem formulations. Only recently, a cost-based filtering algorith...
Meinolf Sellmann
NIPS
2004
15 years 1 months ago
Expectation Consistent Free Energies for Approximate Inference
We propose a novel a framework for deriving approximations for intractable probabilistic models. This framework is based on a free energy (negative log marginal likelihood) and ca...
Manfred Opper, Ole Winther
CAGD
2004
76views more  CAGD 2004»
14 years 11 months ago
Topologically consistent trimmed surface approximations based on triangular patches
Topologically consistent algorithms for the intersection and trimming of free-form parametric surfaces are of fundamental importance in computer-aided design, analysis, and manufa...
Rida T. Farouki, Chang Yong Han, Joel Hass, Thomas...