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Unsupervised Belief Representation Learning in Polarized Networks: A Variational Graph Auto-Encoder Approach
[article]
2022
arXiv
pre-print
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In this paper, we propose an Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) for polarity representation learning in an unsupervised manner. ...
It jointly learns the belief embedding of both users and their claims in the same latent space. ...
CONCLUSION In this paper, we proposed a Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) for polarity representation learning in an unsupervised manner. ...
arXiv:2110.00210v4
fatcat:2dblja5lxjce7ahoclh43jq6xi
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
2019
Data Science and Engineering
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Unsupervised graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. ...
To explore this, we present extensive experimental evaluation with five state-of-the-art unsupervised graph embedding techniques, across a range of empirical graph datasets, measuring a selection of topological ...
Acknowledgements We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. ...
doi:10.1007/s41019-019-0097-5
fatcat:4c6hm5bbnfakbasinxn3dhziwu
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
[article]
2018
arXiv
pre-print
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In this paper, we investigate if graph embeddings are approximating something analogous with traditional vertex level graph features. ...
Graph embedding techniques aim to automatically create a low-dimensional representation of a given graph, which captures key structural elements in the resulting embedding space. ...
Acknowledgments We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research. ...
arXiv:1806.07464v1
fatcat:322vvw7kunfczcvtb4glhjmvqu
Gaussian process decentralized data fusion meets transfer learning in large-scale distributed cooperative perception
2019
Autonomous Robots
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Unsupervised Feature Selection Jun Guo*, Wenwu Zhu Dependence in Propositional Logic: Formula-Formula Dependence and Formula Forgetting â€" Application to Belief Update and Conservative Extension Liangda ...
Gradients Andrew Ross*, Finale Doshi-Velez Improving Variational Encoder-Decoders in Dialogue Generation Xiaoyu Shen*, Hui Su, Vera Demberg, Shuzi Niu IMS-DTM: Incremental Multi-Scale Dynamic Topic Models ...
doi:10.1007/s10514-018-09826-z
fatcat:67yqhwmgozccxni56rxmuapjgm
2021 Index IEEE Signal Processing Letters Vol. 28
2021
IEEE Signal Processing Letters
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
Note that the item title is found only under the primary entry in the Author Index. ...
Xu, W., +, LSP 2021 1570-1574 Discriminative Auto-Encoding for Classification and Representation Learning Problems. ...
doi:10.1109/lsp.2022.3145253
fatcat:a3xqvok75vgepcckwnhh2mty74
A Comprehensive Review of Community Detection in Graphs
[article]
2024
arXiv
pre-print
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Detecting communities in graphs is a challenging problem with applications in sociology, biology, and computer science. ...
The study of complex networks has significantly advanced our understanding of community structures which serves as a crucial feature of real-world graphs. ...
VGAE:Variational Graph Auto-Encoder (Kipf & Welling, 2016b ) is an unsupervised learning framework for graph-structured data that extends the variational auto-encoder (VAE) concept. ...
arXiv:2309.11798v4
fatcat:vu3skmbwuncixlufd3jmfmc4xa
Multi-source knowledge fusion: a survey
2020
World wide web (Bussum)
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, information fusion within KGs, multi-modal knowledge fusion and multi-source knowledge collaborative reasoning. ...
promote the construction of domain knowledge graphs (KGs), and bring enormous social and economic benefits. ...
The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. ...
doi:10.1007/s11280-020-00811-0
fatcat:ef5j2sna6fai7k2455yihrrfuq
Deep Learning Based Text Classification: A Comprehensive Review
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2004.03705v1 fatcat:2jhz46jj6zbctidnhtsfv5fioqOther Versions
In this paper, we provide a comprehensive review of more than 150 deep learning based models for text classification developed in recent years, and discuss their technical contributions, similarities, ...
Deep learning based models have surpassed classical machine learning based approaches in various text classification tasks, including sentiment analysis, news categorization, question answering, and natural ...
This section discusses unsupervised models based on auto-encoders and its variants. Kiros et al. [135] proposed the Skip-Thought model for unsupervised learning of a generic, sentence encoder. ...
arXiv:2004.03705v3
fatcat:al5hstylsbhfpldvokuvlpomam
Table of Contents
2021
IEEE Signal Processing Letters
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Pi Discriminative Auto-Encoding for Classification and Representation Learning Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
Hu Polar Coded Modulation Operated With Physical Network Coding . . . . . . . . . . . ...Z. Xie, P. Chen, R. Chen, and Y. ...
doi:10.1109/lsp.2021.3134549
fatcat:m6obtl7k7zdqvd62eo3c4tptfy
Towards Deep Learning Prospects: Insights for Social Media Analytics
2019
IEEE Access
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INDEX TERMS Social media data, dynamic network, deep learning, feature learning. 36958 He has published about 70 papers in reputed international Impact Factor journals and conferences. ...
Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing. ...
d: AUTO-ENCODER An Auto-Encoder (AE), also known as auto-associator, or Diabolo network is an ANN used for unsupervised learning. ...
doi:10.1109/access.2019.2905101
fatcat:65mxyey3frdrfngvbfnfss3gpa
Autonomous Driving with Deep Learning: A Survey of State-of-Art Technologies
[article]
2020
arXiv
pre-print
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This is a survey of autonomous driving technologies with deep learning methods. ...
Almost at the same time, deep learning has made breakthrough by several pioneers, three of them (also called fathers of deep learning), Hinton, Bengio and LeCun, won ACM Turin Award in 2019. ...
learned from real data using Polar Grid Maps (PGM). ...
arXiv:2006.06091v3
fatcat:nhdgivmtrzcarp463xzqvnxlwq
2021 Index IEEE Transactions on Knowledge and Data Engineering Vol. 33
2022
IEEE Transactions on Knowledge and Data Engineering
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-that appeared in this periodical during 2021, and items from previous years that were commented upon or corrected in 2021. ...
Note that the item title is found only under the primary entry in the Author Index. ...
Zhang, F., +, TKDE March 2021 867-881 March 2021 882-896 Game Theoretical Adversarial Deep Learning With Variational Adversaries. logical Networks. ...
doi:10.1109/tkde.2021.3128365
fatcat:4m5kefreyrbhpb3lhzvgqzm3qu
Hyperbolic Deep Neural Networks: A Survey
[article]
2021
arXiv
pre-print
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Recently, there has been a rising surge of momentum for deep representation learning in hyperbolic spaces due to theirhigh capacity of modeling data like knowledge graphs or synonym hierarchies, possessing ...
We refer to the model as hyperbolic deep neural network in this paper. ...
We also want to thank Emile Mathieu, from University of Oxford, for the explanation regarding the gyroplane layer in their Poincaré Variational Auto-Encoder. ...
arXiv:2101.04562v3
fatcat:yqj4zohrqjbplpsdy5f5uglnbu
Deep Learning Meets SAR
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2006.10027v1 fatcat:sjdqhdimfjdufazmwihs7zftku
With this effort, we hope to stimulate more research in this interesting yet under-exploited research field and to pave the way for use of deep learning in big SAR data processing workflows. ...
Deep learning in remote sensing has become an international hype, but it is mostly limited to the evaluation of optical data. ...
[67] proposed a discriminant DBN (DisDBN) for SAR image classification, in which discriminant features are learned by combining ensemble learning with a deep belief network in an unsupervised manner ...
arXiv:2006.10027v2
fatcat:s3tiroz4qve6nbhavtz77fbis4
Conditional variational autoencoders for probabilistic wind turbine blade fatigue estimation using Supervisory, Control, and Data Acquisition data
2021
Wind Energy
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A Variational Auto-Encoder (VAE) is trained in order to model the probability distribution of the accumulated fatigue on the root cross-section of a simulated wind turbine blade. ...
Data Acquisition (SCADA) data, while capturing the inherent aleatoric uncertainty due to the incomplete information on strain time series of the wind turbine structure, when only SCADA data statistics ...
Auto-Encoder (VAE). ...
doi:10.1002/we.2621
fatcat:qdvugese7nacfbyoywbahqjopy
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