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Centering is a general methodology for accelerating learning in adaptive systems of the type exemplified by neural networks — that is, systems that are typically nonlinear, continuous, and redundant; that learn incrementally from examples, generally by some form of gradient descent.
Here we generalize this notion to all factors involved in the network's gradient, leading us to propose centering the slope of hidden unit activation functions ...
Apr 19, 1997 · Centering is a general methodology for accelerating learning in adaptive systems of the type exemplified by neural networks — that is, systems ...
This work proposes centering the slope of hidden unit activation functions as well, which removes the linear component of backpropagated error and improves ...
Here we generalize this notion to all factors involved in the network's gradient, leading us to propose centering the slope of hidden unit activation functions ...
Recommendations · Centering Neural Network Gradient Factors · Universality of gradient descent neural network training. Abstract · Convergence of gradient method ...
Schraudolph [14, 13] proposed centering all factors in the gradient to have zero mean. This lead to a significant speed-up in learning when using shortcut ...
May 20, 1998 · Gradient factor centering is a new methodology for decompos- ing neural networks into biased and centered subnets which are then trained in ...
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Aug 13, 2019 · Each hyperplane is the locus of points where the net-input to the hidden unit is zero and is thus the classification boundary generated by that ...
Missing: Factors. | Show results with:Factors.
Mar 25, 2022 · Centered Weight Normalization in Accelerating Training of Deep Neural Networks. ... Centering Neural Network Gradient Factors. In G. B. Orr & K.-R ...