Results 21 to 30 of about 34,284 (266)
Fault diagnosis of mind-used transformer based on stacked sparse auto-encoder
Gong-kuang zidonghua, 2018 In view of application of deep learning to transformer fault diagnosis had a good fault diagnosis effect, a fault diagnosis method of mind-used transformer based on stacked sparse auto-encoder was proposed.
XU Qianwen +4 more
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PFVAE: A Planar Flow-Based Variational Auto-Encoder Prediction Model for Time Series Data
Mathematics, 2022 Prediction based on time series has a wide range of applications. Due to the complex nonlinear and random distribution of time series data, the performance of learning prediction models can be reduced by the modeling bias or overfitting.
Xue-Bo Jin +4 more
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Traffic Request Generation through a Variational Auto Encoder Approach
Computers, 2022 Traffic and transportation forecasting is a key issue in urban planning aimed to provide a greener and more sustainable environment to residents. Their privacy is a second key issue that requires synthetic travel data.
Stefano Chiesa, Sergio Taraglio
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Ensemble graph auto-encoders for clustering and link prediction [PDF]
PeerJ Computer ScienceGraph auto-encoders are a crucial research area within graph neural networks, commonly employed for generating graph embeddings while minimizing errors in unsupervised learning.
Chengxin Xie +5 more
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, 2020 It has been conjectured that the Fisher divergence is more robust to model uncertainty than the conventional Kullback-Leibler (KL) divergence. This motivates the design of a new class of robust generative auto-encoders (AE) referred to as Fisher auto-encoders.
Elkhalil, Khalil +4 more
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Transforming Auto-Encoders [PDF]
, 2011 The artificial neural networks that are used to recognize shapes typically use one or more layers of learned feature detectors that produce scalar outputs. By contrast, the computer vision community uses complicated, hand-engineered features, like SIFT [6], that produce a whole vector of outputs including an explicit representation of the pose of the ...
Geoffrey E. Hinton +2 more
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Monte Carlo Variational Auto-Encoders
, 2021 Variational auto-encoders (VAE) are popular deep latent variable models which are trained by maximizing an Evidence Lower Bound (ELBO). To obtain tighter ELBO and hence better variational approximations, it has been proposed to use importance sampling to get a lower variance estimate of the evidence.
Thin, A +5 more
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Random Walk Graph Auto-Encoders With Ensemble Networks in Graph Embedding
IEEE Access, 2023 Recently graph auto-encoders have received increasingly widespread attention as one of the important models in the field of deep learning. Existing graph auto-encoder models only use graph convolutional neural networks (GCNs) as encoders to learn the ...
Chengxin Xie +3 more
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NLGAE:A Graph Autoencoder Model Based on Improved Network Structure and Loss Functionfor Node Classification Task [PDF]
Jisuanji kexueThe universally accepted technique to address the issues of computational complexity and high spatial complexity of adjacency matrix due to non-Euclidean spatiality of graph data is to use graph embedding methods to map high-dimensional heterogeneous ...
LIAO Bin, ZHANG Tao, YU Jiong, LI Min
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Human-Centric Intelligent Systems, 2022 Building up a map is essential for mobile robots to localize their position and perfect autonomous navigation which is known as Simultaneous Localization and Mapping (SLAM).
Md. Tariqul Islam +5 more
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