Results 51 to 60 of about 83,749 (307)
CoRR, 2019 Proceedings of the 37th International Conference on Machine ...
Michael Moor +3 more
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Improving Performance of Autoencoder-Based Network Anomaly Detection on NSL-KDD Dataset
IEEE Access, 2021 Network anomaly detection plays a crucial role as it provides an effective mechanism to block or stop cyberattacks. With the recent advancement of Artificial Intelligence (AI), there has been a number of Autoencoder (AE) based deep learning approaches ...
Wen Xu +4 more
semanticscholar +1 more source
Detection of Pitting in Gears Using a Deep Sparse Autoencoder
Applied Sciences, 2017 In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network.
Yongzhi Qu +3 more
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Error correction algorithm of array time-varying amplitude and phase based on autoencoder
Xibei Gongye Daxue Xuebao, 2023 As array antennas are widely used in various mobile platforms, the time-varying amplitude and phase error has become an important factor affecting the application of array signal processing technology.
ZHANG Zixuan +3 more
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An Introduction to Autoencoders
CoRR, 2022 In this article, we will look at autoencoders. This article covers the mathematics and the fundamental concepts of autoencoders. We will discuss what they are, what the limitations are, the typical use cases, and we will look at some examples. We will start with a general introduction to autoencoders, and we will discuss the role of the activation ...
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Multiresolution convolutional autoencoders
Journal of Computational Physics, 2023 20 pages, 11 ...
Yuying Liu 0010 +3 more
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Deep Autoencoder Neural Networks: A Comprehensive Review and New Perspectives
Archives of Computational Methods in EngineeringAutoencoders have become a fundamental technique in deep learning (DL), significantly enhancing representation learning across various domains, including image processing, anomaly detection, and generative modelling.
Ibomoiye Domor Mienye, Theo G. Swart
semanticscholar +1 more source
Single-cell RNA-seq denoising using a deep count autoencoder
Nature Communications, 2018 Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at a cellular resolution. However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-
Gökçen Eraslan +4 more
semanticscholar +1 more source
Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results
IEEE AccessThis paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed structures include four stages: image preprocessing, feature extraction using autoencoder derivatives,
Azin Masoudi, Majid Ahmadi
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