Jul 16, 2019 · In this paper, we propose a localized stochastic sensitive AE (LiSSA) to enhance the robustness of AE with respect to input perturbations. With ...

Abstract—The training of autoencoder (AE) focuses on the selection of connection weights via a minimization of both the training error and a regularized ...

A localized stochastic sensitive AE (LiSSA) is proposed to enhance the robustness of AE with respect to input perturbations to outperform several classical ...

Jan 4, 2024 · In this paper, we propose a localized stochastic sensitive AE (LiSSA) to enhance the robustness of AE with respect to input perturbations. With ...

This paper presents a short-term electric load forecasting model based on deep autoencoder with localized stochastic sensitivity (D-LiSSA). D-LiSSA can ...

LiSSA : Localized Stochastic Sensitive Autoencoders. Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal.

Overview of D-LiSSA for load forecasting. Time window1. Training data. Validation data. Sliding window. Decoder.

Jan 4, 2024 · This paper presents a short-term electric load forecasting model based on deep autoencoder with localized stochastic sensitivity (D-LiSSA).

Mar 8, 2020 · In this work, a radial basis function neural network (RBFNN) with localized stochastic-sensitive autoencoder (LiSSA) is proposed to solve this ...

Mar 18, 2021 · This paper presents a short-term electric load forecasting model based on deep autoencoder with localized stochastic sensitivity (D-LiSSA). D- ...