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Natural Gradient and Multiclass NLDA Networks
2002Natural gradient has been recently introduced as a method to improve the convergence of Multilayer Perceptron (MLP) training [1] as well as that of other neural network type algorithms. The key idea is to recast the training process as a problem in quasi maximum log—likelihood estimation of a certain semipara-metric probabilistic model. This allows the
José R. Dorronsoro, Ana M. González
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Natural Gradient Learning in NLDA Networks
2001Neural network training is usually formulated as a problem in function minimization. More precisely, if W are the weights defining a network’s architecture And e(W) is the weight depending error function, its gradient ∇e(W) is usually employed to arrive at the optimal weight set W*.
José R. Dorronsoro +2 more
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Fast Global Convergence of Natural Policy Gradient Methods With Entropy Regularization
Operations Research, 2022Yuxin Chen, Yuejie Chi, Yuting Wei
exaly
On the Locality of the Natural Gradient for Learning in Deep Bayesian Networks
Information Geometry, 2023Nihat Ay
exaly
Variational Bayesian Kalman Filter Using Natural Gradient
Chinese Journal of Aeronautics, 2022William Moran, Zhentao Hu, Xuezhi Wang
exaly
Soft Tissue Sarcomas: Classification and Natural History
Ca-A Cancer Journal for Clinicians, 1981Steven I Hajdu, S I Hajdu
exaly
Natural History and Diagnostic Peculiarities of Acute Leukemia
Ca-A Cancer Journal for Clinicians, 1964Carl V Moore
exaly
Benign Soft Tissue Tumors: Classification and Natural History
Ca-A Cancer Journal for Clinicians, 1987Steven I Hajdu, S I Hajdu
exaly
The Natural History of Untreated Lung Cancer
Ca-A Cancer Journal for Clinicians, 1964Leo G Rigler
exaly