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ICAI
2007
15 years 1 months ago
Dynamic Programming Algorithm for Training Functional Networks
Abstract— The paper proposes a dynamic programming algorithm for training of functional networks. The algorithm considers each node as a state. The problem is formulated as find...
Emad A. El-Sebakhy, Salahadin Mohammed, Moustafa E...
ICANN
2010
Springer
15 years 24 days ago
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines
Abstract. Learning algorithms relying on Gibbs sampling based stochastic approximations of the log-likelihood gradient have become a common way to train Restricted Boltzmann Machin...
Asja Fischer, Christian Igel
HAIS
2009
Springer
15 years 4 months ago
Pareto-Based Multi-output Model Type Selection
In engineering design the use of approximation models (= surrogate models) has become standard practice for design space exploration, sensitivity analysis, visualization and optimi...
Dirk Gorissen, Ivo Couckuyt, Karel Crombecq, Tom D...
ATAL
2008
Springer
15 years 1 months ago
Approximating power indices
Many multiagent domains where cooperation among agents is crucial to achieving a common goal can be modeled as coalitional games. However, in many of these domains, agents are une...
Yoram Bachrach, Evangelos Markakis, Ariel D. Proca...
ICIP
2007
IEEE
16 years 1 months ago
Iterative Blind Image Motion Deblurring via Learning a No-Reference Image Quality Measure
In this paper, we propose a learning-based image restoration algorithm for restoring images degraded by uniform motion blurs. The motion blur parameters are first approximately es...
Wen-Hao Lee, Shang-Hong Lai, Chia-Lun Chen