Hessian with Mini-Batches for Electrical Demand Prediction
Israel Elias,José de Jesús Rubio,David Ricardo Cruz,Genaro Ochoa,Juan Francisco Novoa,Dany Ivan Martinez,Samantha Muñiz,Ricardo Balcazar,Enrique Garcia,Cesar Felipe Juarez +9 more
TLDR
The Hessian is combined with mini-batches for neural network tuning and the discussed algorithm is applied for electrical demand prediction.Abstract:
The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction.read more
Citations
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Posted Content
Newton-Type Methods for Non-Convex Optimization Under Inexact Hessian Information
TL;DR: The canonical problem of finite-sum minimization is considered, and appropriate uniform and non-uniform sub-sampling strategies are provided to construct such Hessian approximations, and optimal iteration complexity is obtained for the correspondingSub-sampled trust-region and adaptive cubic regularization methods.