Suchergebnisse - Autoencoder application

Slow feature‐constrained decomposition autoencoder: Application to process anomaly detection and localization von Jia, Mingwei, Jiang, Lingwei, Hu, Junhao, Liu, Yi, Chen, Tao

“… To address this challenge, we propose a slow feature‐constrained decomposition autoencoder (SFC‐DAE …”
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An overview and empirical comparison of natural language processing (NLP) models and an introduction to and empirical application of autoencoder models in marketing von Shankar, Venkatesh, Parsana, Sohil

“… ) models, have surged in popularity for analyzing unstructured data in marketing. Yet, we do not fully understand which NLP models are appropriate for which marketing applications and what insights can be best derived …”
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Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology von Janowczyk, Andrew, Basavanhally, Ajay, Madabhushi, Anant

“… •Stain Normalization using Sparse AutoEncoders (StaNoSA) in introduced.•It standardizes color distributions of a test image to a single template image …”
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Optimizing Large Scale Problems With Metaheuristics in a Reduced Space Mapped by Autoencoders-Application to the Wind-Hydro Coordination von Miranda, Vladimiro, da Hora Martins, Joana, Palma, Vera

“… The technique applies autoencoders as a reversible mapping between the original problem space and a reduced space …”
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Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design von Mak, Hugo Wai Leung, Han, Runze, Yin, Hoover H. F.

“… In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capability in image generation and dimensionality reduction …”
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Disentangled-Multimodal Adversarial Autoencoder: Application to Infant Age Prediction With Incomplete Multimodal Neuroimages von Hu, Dan, Zhang, Han, Wu, Zhengwang, Wang, Fan, Wang, Li, Smith, J. Keith, Lin, Weili, Li, Gang, Shen, Dinggang

“… Effective fusion of structural magnetic resonance imaging (sMRI) and functional magnetic resonance imaging (fMRI) data has the potential to boost the accuracy …”
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A comprehensive survey on design and application of autoencoder in deep learning von Li, Pengzhi, Pei, Yan, Li, Jianqiang

“… Researchers have proposed several improved versions of autoencoder based on different application fields …”
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SDCA: a novel stack deep convolutional autoencoder – an application on retinal image denoising von Ghosh, Swarup Kr, Biswas, Biswajit, Ghosh, Anupam

“… This study represents a deep learning based approach to denoising images and restoring features using stack denoising convolutional autoencoder …”
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A new investment method with AutoEncoder: Applications to crypto currencies von Nakano, Masafumi, Takahashi, Akihiko

“… •AutoEncoder extracts the factors which enable to prevent the large drawdown.•The extracted non-linear factors are implemented by the dynamic delta hedging …”
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A multi‐feature space constrained stacked autoencoder and its application for uncertain process monitoring von Yang, Jiandong, Wang, Chenhao, Yu, Jianbo, Yan, Xuefeng

“… To mitigate these issues, we propose a novel process monitoring method based on a multi‐feature space constrained stacked autoencoder (MFSCSAE …”
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Towards an Interpretable Autoencoder: A Decision-Tree-Based Autoencoder and its Application in Anomaly Detection von Aguilar, Diana Laura, Medina-Perez, Miguel Angel, Loyola-Gonzalez, Octavio, Choo, Kim-Kwang Raymond, Bucheli-Susarrey, Edoardo

“… Furthermore, our proposed interpretable autoencoder provides a natural explanation for experts in the application area …”
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Autoencoders and their applications in machine learning: a survey von Berahmand, Kamal, Daneshfar, Fatemeh, Salehi, Elaheh Sadat, Li, Yuefeng, Xu, Yue

“… Autoencoders have become a hot researched topic in unsupervised learning due to their ability to learn data features and act as a dimensionality reduction method …”
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Autoencoder Application for Artwork Authentication Fingerprinting Using the Craquelure Network von Chirosca, Gianina, Radvan, Roxana, Pop, Matei, Chirosca, Alecsandru

“… This paper presents a deep learning-based system designed for generating, storing, and retrieving embeddings, specifically tailored for analyzing craquelure …”
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Development of a novel parallel framework upon deep dual‐enhanced autoencoder and its applications for industrial soft sensing von Chen, Xu, Shao, Weiming, Wei, Chihang

“… In recent years, data‐driven soft sensing technology has provided a cost‐effective support for industrial process monitoring, in which autoencoder plays an important role in extracting features for soft sensing technology …”
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Application of Generative Autoencoder in De Novo Molecular Design von Blaschke, Thomas, Olivecrona, Marcus, Engkvist, Ola, Bajorath, Jürgen, Chen, Hongming

“… In this work, we investigate the potential use of autoencoder, a deep learning methodology, for de novo molecular design …”
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Dual-Masked Autoencoders: Application to Multi-labeled Pediatric Thoracic Diseases von Yoon, Taeyoung, Kang, Daesung

“… To overcome this limitation, we introduce the dual-masked autoencoders (dual-MAE) algorithm, consisting of online and target networks with encoder and decoder modules …”
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A Pansharpening Based on the Non-Subsampled Contourlet Transform and Convolutional Autoencoder: Application to QuickBird Imagery von Smadi, Ahmad Al, Yang, Shuyuan, Abugabah, Ahed, Alzubi, Ahmad Ali, Sanzogni, Louis

“… ) and convolutional autoencoder (CAE). NSCT is exceptionally proficient at presenting orientation information and capturing the internal geometry of objects …”
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Autoencoder Application for Anomaly Detection in Power Consumption of Lighting Systems von Smialkowski, Tomasz, Czyzewski, Andrzej

“… This paper examines algorithms based on the deep learning method using the Autoencoder model with LSTM and 1D Convolutional networks for various configurations and training periods …”
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Anomaly detection for structural formation analysis by autoencoders: application to soft matters von Terao, Takamichi

“… To solve this problem, an anomaly detection method that uses an autoencoder (AE) to distinguish systems with unknown structures was developed …”
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Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection von Ahmed, Imtiaz, Galoppo, Travis, Hu, Xia, Ding, Yu

“… Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding …”
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