LSTM-Autoencoder Deep Learning Model for Anomaly Detection in Electric Motor
EnergiesAnomaly detection is the process of detecting unusual or unforeseen patterns or events in data. Many factors, such as malfunctioning hardware, malevolent activities, or modifications to the data’s underlying distribution, might cause anomalies.
Fadhila Lachekhab +4 more
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Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning [PDF]
IEEE International Conference on Computer Vision, 2022 Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales.
Colorado Reed +8 more
semanticscholar +1 more source
In-context Autoencoder for Context Compression in a Large Language Model [PDF]
International Conference on Learning Representations, 2023 We propose the In-context Autoencoder (ICAE), leveraging the power of a large language model (LLM) to compress a long context into short compact memory slots that can be directly conditioned on by the LLM for various purposes.
Tao Ge +4 more
semanticscholar +1 more source
Deep Medical Image Reconstruction with Autoencoders using Deep Boltzmann Machine Training [PDF]
EAI Endorsed Transactions on Pervasive Health and Technology, 2020 INTRODUCTION: Deep learning-based Image compression achieves a promising result in recent years as compared with the traditional transform coding methodology.
Saravanan. S, Sujitha Juliet
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Abnormal Network Traffic Detection Method Combining Mahalanobis Distance and Autoencoder [PDF]
Jisuanji gongcheng, 2022 The existing abnormal traffic detection methods are limited in the accuracy due to the large scale of network traffic data and its imbalanced distribution.To address the problem, a method combining Mahalanobis distance and autoencoder is proposed to ...
LI Beibei, PENG Li, DAI Feifei
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Representation Learning: Recommendation With Knowledge Graph via Triple-Autoencoder
Frontiers in Genetics, 2022 The last decades have witnessed a vast amount of interest and research in feature representation learning from multiple disciplines, such as biology and bioinformatics.
Yishuai Geng +3 more
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Dual Autoencoder Network with Separable Convolutional Layers for Denoising and Deblurring Images
Journal of Imaging, 2022 A dual autoencoder employing separable convolutional layers for image denoising and deblurring is represented. Combining two autoencoders is presented to gain higher accuracy and simultaneously reduce the complexity of neural network parameters by using ...
Elena Solovyeva, Ali Abdullah
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Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection [PDF]
IEEE International Conference on Computer Vision, 2019 Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal inputs than the normal ones, which is adopted as a criterion for ...
Dong Gong +6 more
semanticscholar +1 more source
A graph convolutional autoencoder approach to model order reduction for parametrized PDEs [PDF]
Journal of Computational Physics, 2023 The present work proposes a framework for nonlinear model order reduction based on a Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) context, one is interested in obtaining real-time and many-query evaluations of parametric
F. Pichi, B. Moya, J. Hesthaven
semanticscholar +1 more source
Autoencoder untuk Sistem Prediksi Berat Lahir Bayi
Jurnal Teknologi Informasi dan Ilmu Komputer, 2022 Salah satu ukuran terpenting saat awal persalinan adalah keakuratan prediksi berat lahir. Dengan menggunakan metode prediksi yang tepat, perkiraan ekstrim berat lahir bayi dapat dideteksi lebih atau kurang sehingga beberapa tindakan pencegahan dapat ...
Fitra Septia Nugraha +1 more
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