Tatsuya Cho

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Node perturbation learning is a stochastic gradient descent method for neural networks. It estimates the gradient by comparing an evaluation of the perturbed output and the unperturbed output performance, which we call the baseline. Node perturbation learning has primarily been investigated without taking noise on the baseline into consideration. In real(More)
Node perturbation learning has been receiving much attention as a method for achieving stochastic gradient descent. As it does not require direct gradient calculations, it can be applied to a reinforcement learning framework. However, in conventional node perturbation learning, the residual error due to perturbation is not eliminated even after convergence.(More)
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