Výsledky vyhledávání - "Graph variational autoencoder"

Graph variational autoencoder with affinity propagation for community-aware anomaly detection in attributed networks Autor Cao, Zhijie, Yang, Chengkun, Fan, Xiaoqing, Li, Lingjie, Lin, Qiuzhen, Li, Jianqiang, Ma, Lijia

“…Anomaly detection in attributed networks (ADAN) aims to identify abnormal nodes that exhibit unexpected link structures and attributes compared to the others…”
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Objective-directed deep graph generative model for automatic and intelligent highway interchange design Autor Ma, Chenxiang, Xu, Chengcheng

“…Highway interchanges have traditionally been designed through a time-consuming manual process. To enhance efficiency and effectiveness, this paper develops an…”
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Adaptive Latent Graph Representation Learning for Image-Text Matching Autor Tian, Mengxiao, Wu, Xinxiao, Jia, Yunde

“…Image-text matching is a challenging task due to the modality gap. Many recent methods focus on modeling entity relationships to learn a common embedding space…”
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High-Speed Link System Surrogate Modeling and Co-Optimization Based on Multimodal Machine Learning and GraphVAE Autor Wu, Xiao-Yang, Wang, Da-Wei, Zhao, Wen-Sheng

“…With the continuous advancement of very-large-scale integra tion technology, high-speed interconnect systems are increasingly challenged by signal integrity…”
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A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network Autor Rico, Julian Carvajal, Alaeddini, Adel, Faruqui, Syed Hasib Akhter, Fisher-Hoch, Susan P, Mccormick, Joseph B

“…Predicting the emergence of multiple chronic conditions (MCC) is crucial for early intervention and personalized healthcare, as MCC significantly impacts…”
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MAGVA: An Open-Set Fault Diagnosis Model Based on Multi-Hop Attentive Graph Variational Autoencoder for Autonomous Vehicles Autor Fu, Rao, Bi, Yuanguo, Han, Guangjie, Zhang, Xiaoling, Liu, Li, Zhao, Liang, Hu, Bing

“…To improve the reliability of autonomous vehicles, open-set fault diagnosis is indispensable to jointly detect known and unknown faults, in which unknown…”
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Automated site layout generation for buildings using graph constrained generative adversarial network Autor Jiang, Ming, Chen, Yimin, Liu, Xiaoping, Gao, Jinding

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The Deep Latent Position Block Model for Block Clustering and Latent Representation of Nodes in Networks Autor Boutin, Rémi, Latouche, Pierre, Bouveyron, Charles

“…The current surge in data has led to a significant increase in the size of networks used to model relationships between different objects represented as nodes…”
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Compressed graph representation for scalable molecular graph generation Autor Kwon, Youngchun, Lee, Dongseon, Choi, Youn-Suk, Shin, Kyoham, Kang, Seokho

“…Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases…”
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piRNA-disease association prediction based on multi-channel graph variational autoencoder Autor Sun, Wei, Guo, Chang, Wan, Jing, Ren, Han

“…Piwi-interacting RNA (piRNA) is a type of non-coding small RNA that is highly expressed in mammalian testis. PiRNA has been implicated in various human…”
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Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs Autor Gunduz, Hakan

“…Malware harms the confidentiality and integrity of the information that causes material and moral damages to institutions or individuals. This study proposed a…”
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Deep Latent-Variable Models for Controllable Molecule Generation Autor Du, Yuanqi, Wang, Yinkai, Alam, Fardina, Lu, Yuanjie, Guo, Xiaojie, Zhao, Liang, Shehu, Amarda

“…Representation learning via deep generative models is opening a new avenue for small molecule generation in silico. Linking chemical and biological space…”
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Latent Space Neural Architecture Search via LambdaNDCGloss-Based Listwise Ranker Autor Xiao, Songyi, Zhao, Bo, Liu, Derong

“…The rapid development of neural architecture search (NAS) promotes the research of efficient evaluation of candidate architectures. However, as one of highly…”
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BiAtt-GVAE: Molecular Design for Specific Target via Graph Variational Autoencoder Based on Bi-Channel Interactive Attention Network Autor Ma, Mei, Lei, Xiujuan

“…Designing bioactive molecules with desired properties for specific targets is a longstanding challenge in drug design. We introduce a model called BiAtt-GVAE,…”
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MoleculeXpert: A Novel Architecture for Expert-Level Molecule Analysis of HIV Inhibition Autor Salim, Ashik P, Naseer, Raed, Thottunkal, Rajeev, S, Vishnu Prasad, Varghese, Jina

“…The global HIV/AIDS pandemic persists as a formidable health challenge, demanding innovative solutions for accelerated drug discovery. In response to this…”
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A deep learning algorithm for predicting protein-protein interactions with nonnegative latent factorization Autor Wang, Liwei, Hu, Lun

“…Protein-protein interaction (PPI) networks play an essential role in the study of proteomics. Given the fact that known PPI data are extremely incomplete,…”
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FastGAE: Scalable graph autoencoders with stochastic subgraph decoding Autor Salha, Guillaume, Hennequin, Romain, Remy, Jean-Baptiste, Moussallam, Manuel, Vazirgiannis, Michalis

“…Graph autoencoders (AE) and variational autoencoders (VAE) are powerful node embedding methods, but suffer from scalability issues. In this paper, we introduce…”
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Multiresolution equivariant graph variational autoencoder Autor Hy, Truong Son, Kondor, Risi

“…In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate…”
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