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ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient

23 Dec 2014 • Caglar Gulcehre • Marcin Moczulski • Yoshua Bengio

Stochastic gradient algorithms have been the main focus of large-scale learning problems and they led to important successes in machine learning. The convergence of SGD depends on the careful choice of learning rate and the amount of the noise in stochastic estimates of the gradients... (read more)

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METHOD TYPE
SGD
Stochastic Optimization
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