Results 11 to 20 of about 3,760 (232)
Deep Learning-Based 3D Reconstruction for Defect Detection in Shipbuilding Sub-Assemblies [PDF]
SensorsOvershooting defects in shipbuilding subassemblies are essential to ensure the final product’s overall integrity and safety. In this work, we focus on the automatic detection of overshooting defects in simple and T-shaped sub-assemblies by employing ...
Paula Arcano-Bea +5 more
doaj +2 more sources
HeliyonSingle-cell RNA sequencing (scRNA-seq) enables high-resolution transcriptional profiling of cell heterogeneity. However, analyzing this noisy, high-dimensional matrix remains challenging.
Somayyeh Koohi
exaly +3 more sources
MachinesSolar energy is a critical renewable energy source, with solar arrays or photovoltaic systems widely used to convert solar energy into electrical energy. However, solar array systems can develop faults and may exhibit poor performance.
Andrei Petrovski +2 more
exaly +3 more sources
Epitomic Variational Graph Autoencoder [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 Variational autoencoder (VAE) is a widely used generative model for learning latent representations. Burda et al. in their seminal paper showed that learning capacity of VAE is limited by over-pruning. It is a phenomenon where a significant number of latent variables fail to capture any information about the input data and the corresponding hidden ...
Rayyan Ahmad Khan +2 more
openaire +4 more sources
Link Activation Using Variational Graph Autoencoders [PDF]
IEEE Communications Letters, 2021 An unsupervised method is proposed for link activation in wireless networks by identifying clusters of interfering users. A k-nearest neighbors interference graph is first defined for the wireless network which is then mapped to a stochastic latent space. The users are then clustered in the latent space using a Gaussian mixture model, and one user from
Saeed Jamshidiha +3 more
openaire +2 more sources
Variational Graph Normalized AutoEncoders [PDF]
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021 Link prediction is one of the key problems for graph-structured data. With the advancement of graph neural networks, graph autoencoders (GAEs) and variational graph autoencoders (VGAEs) have been proposed to learn graph embeddings in an unsupervised way. It has been shown that these methods are effective for link prediction tasks.
Seong-Jin Ahn 0002, Myoung Ho Kim
openaire +2 more sources
A Variational Graph Autoencoder for Manipulation Action Recognition and Prediction [PDF]
2021 20th International Conference on Advanced Robotics (ICAR), 2021 Accepted for publication in the Proceedings of 2021 20th International Conference on Advanced Robotics (ICAR)
Gamze Akyol +2 more
openaire +3 more sources
Dirichlet Graph Variational Autoencoder
CoRR, 2020 Inproceedings of NeurIPS ...
Jia Li 0009 +7 more
openaire +3 more sources
Dynamic Joint Variational Graph Autoencoders [PDF]
, 2020 Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for static graphs mainly and cannot capture
Sedigheh Mahdavi +2 more
openaire +2 more sources
Graph Regularized Variational Ladder Networks for Semi-Supervised Learning
IEEE Access, 2020 To tackle the problem of semi-supervised learning (SSL), we propose a new autoencoder-based deep model. Ladder networks (LN) is an autoencoder-based method for representation learning which has been successfully applied on unsupervised learning and semi ...
Cong Hu, Xiao-Ning Song
doaj +1 more source