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Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder. [PDF]
PLoS ONEELM (Extreme learning machine) has drawn great attention due its high training speed and outstanding generalization performance. To solve the problem that the long training time of kernel ELM auto-encoder and the difficult setting of the weight of kernel
Liang Yixuan
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Symbolic expression generation via variational auto-encoder [PDF]
PeerJ Computer Science, 2023 There are many problems in physics, biology, and other natural sciences in which symbolic regression can provide valuable insights and discover new laws of nature. Widespread deep neural networks do not provide interpretable solutions.
Sergei Popov +4 more
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Time-feature attention-based convolutional auto-encoder for flight feature extraction [PDF]
Scientific Reports, 2023 Quick Access Recorders (QARs) provide an important data source for Flight Operation Quality Assurance (FOQA) and flight safety. It is generally characterized by large volume, high-dimensionality and high frequency, and these features result in extreme ...
Qixin Wang +4 more
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Wavelet Loss Function for Auto-Encoder
IEEE Access, 2021 In the field of image generation, especially for auto-encoder models, how to extract better features and obtain better quality reconstruction samples by modifying network structure and training algorithms has always been the focus of attention.
Qiuyu Zhu, Hu Wang, Ruixin Zhang
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Auto-Encoders in Deep Learning—A Review with New Perspectives
Mathematics, 2023 Deep learning, which is a subfield of machine learning, has opened a new era for the development of neural networks. The auto-encoder is a key component of deep structure, which can be used to realize transfer learning and plays an important role in both
Shuangshuang Chen, Wei Guo
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Melting Reduction Auto-Encoder
Jisuanji kexue yu tansuo, 2021 Auto-encoder (AE) is one of the simple and widely used unsupervised feature extraction algorithms of deep learning. Existing automatic encoders for image feature extraction remain some problems such as insufficient feature extraction and excessive model ...
SUN Yu, WEI Benzheng, LIU Chuan, ZHANG Kuixing, CONG Jinyu
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Latent code-based fusion: A Volterra neural network approach
Intelligent Systems with Applications, 2023 We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a latent representation of multi-modal data whose features are jointly captured by a union of subspaces.
Sally Ghanem +2 more
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Aerospace, 2023 This paper aims to build a Self-supervised Fault Detection Model for UAVs combined with an Auto-Encoder. With the development of data science, it is imperative to detect UAV faults and improve their safety. Many factors affect the fault of a UAV, such as
Shenghan Zhou +4 more
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Multi-Scale Auto-Encoder for Edge Detection
IEEE Access, 2022 A multi-scale encoder algorithm is proposed for image edge detection, which takes the auto-encoder as basic backbone structure. Three auto-encoders, each is responsible for processing an image of one scale, are organized together to perform image-to ...
Changyou Shi +5 more
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3D Point Capsule Networks [PDF]
, 2018 In this paper, we propose 3D point-capsule networks, an auto-encoder designed to process sparse 3D point clouds while preserving spatial arrangements of the input data.
Birdal, Tolga +3 more
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