Výsledky vyhledávání - variational graph conventional autoencoder

Multi-objective drug design with a scaffold-aware variational autoencoder Autor Dong, Tiejun, You, Linlin, Chen, Calvin Yu-Chian

“… To tackle this, we have developed ScafVAE, an innovative scaffold-aware variational autoencoder designed for the in silico graph-based generation of multi-objective drug candidates…”
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Heterogeneous Hypergraph Variational Autoencoder for Link Prediction Autor Fan, Haoyi, Zhang, Fengbin, Wei, Yuxuan, Li, Zuoyong, Zou, Changqing, Gao, Yue, Dai, Qionghai

“… of nodes, which tends to lead to a sub-optimal embedding result. This paper presents a method named Heterogeneous Hypergraph Variational Autoencoder (HeteHG-VAE…”
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Entropy-enhanced batch sampling and conformal learning in VGAE for physics-informed causal discovery and fault diagnosis Autor Modirrousta, Mohammadhossein, Memarian, Alireza, Huang, Biao

“…) in complex industrial processes. This research introduces a novel approach to causal discovery and FDD using Variational Graph Autoencoders (VGAEs…”
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MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data Autor Zhao, Jinhui, He, Jiarui, Guan, Pengwei, Bao, Han, Zhao, Xinjie, Zhao, Chunxia, Qin, Wangshu, Lu, Xin, Xu, Guowang

“… To address these critical limitations, we introduce MODAPro, a biologically informed deep learning framework that synergistically integrates variational graph autoencoders (VAE…”
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GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction Autor Xuan, Ping, Fan, Mengsi, Cui, Hui, Zhang, Tiangang, Nakaguchi, Toshiya

“… First, a framework based on graph convolutional autoencoder is constructed to learn attention-enhanced topological embedding that integrates the topology structure…”
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Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data Autor Choi, Seung-Hwan, An, Dawn, Lee, Inho, Lee, Suwoong

“… To address this issue, we employ a semi-supervised learning approach that relies solely on normal data to effectively detect abnormal patterns, overcoming the limitations of conventional methods…”
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Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays Autor Arifeen, Murshedul, Petrovski, Andrei, Hasan, Md Junayed, Noman, Khandaker, Navid, Wasib Ul, Haruna, Auwal

“… This paper introduces a deep learning model that combines a graph convolutional network with a variational autoencoder to diagnose faults in solar arrays…”
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GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction Autor Hui Cui, Toshiya Nakaguchi, Ping Xuan, Mengsi Fan, Tiangang Zhang

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Self-Organizing Maps-Assisted Variational Autoencoder for Unsupervised Network Anomaly Detection Autor Huang, Hailong, Yang, Jiahong, Zeng, Hang, Wang, Yaqin, Xiao, Liuming

“… To overcome these limitations, this study proposes a self-organizing maps-assisted variational autoencoder (SOVAE) framework…”
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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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In-chip artificial intelligence technology for generating and self-correcting the topology of low-consumption RC filters Autor Zainab Al-Araji

“…This study presents an innovative self-tuning system for first-class RC filter circuits, specially designed to achieve a target cut-off frequency of 500 kHz…”
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TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation Autor Ahmad, Sohail, Wang, Hong

“… While the Confounding Cascade Nonlinear Additive Noise Model (CCANM) coupled with variational autoencoders (VAEs…”
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Enhancing Facial Reconstruction Using Graph Attention Networks Autor Lee, Hyeong Geun, Hur, Jee Sic, Yoon, Yeo Chan, Kim, Soo Kyun

“… By contrast, restoration methods utilizing Graph Convolution Networks (GCN) offer the advantages of non-linearity and direct regression of vertex coordinates and colors…”
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Open-world structured sequence learning via dense target encoding Autor Zhang, Qin, Liu, Ziqi, Li, Qincai, Xiang, Haolong, Yu, Zhizhi, Chen, Junyang, Zhang, Peng, Chen, Xiaojun

“…Structured sequences are popularly used to describe graph data with time-evolving node features and edges…”
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A densely connected framework for cancer subtype classification Autor Li, Yu, Zheng, Denggao, Sun, Kaijie, Qin, Chi, Duan, Yuchen, Zhou, Qingqing, Yin, Yunxia, Kan, Hongxing, Hu, Jili

“… Results We propose DEGCN, a novel deep learning model that integrates a three-channel Variational Autoencoder (VAE…”
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Hybrid Variational Autoencoder for Collaborative Filtering Autor Liu, Jinxin, Xiao, Yingyuan, Zhu, Ke, Zheng, Wenguang, Hsu, Ching-Hsien

“…In recent years, Variational AutoEncoder (VAE) based methods have made many important achievements in the field of collaborative filtering recommendation system…”
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Epileptic Seizure Detection from EEG Signals using Autoencoder-based Graph Convolutional Neural Network Autor Jibon, Ferdaus Anam, Hasan Siddiqui, Fazlul

“… In this study, a novel hybrid framework is proposed for detecting epileptic seizures using a graph convolutional neural network (GCNN…”
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Author Name Disambiguation Based on Multi Relationship Fusion and Representation Learning Autor Cui, Huanqing, Huang, Qian, Yang, Junzhu, Liu, Ruixia, Hu, Kekun, Dong, Gang

“…), learns unified paper representations using Variational Graph Autoencoder (VGAE), and produces final clusters through Hierarchical Agglomerative Clustering (HAC…”
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Quantum deep learning-enhanced ethereum blockchain for cloud security: intrusion detection, fraud prevention, and secure data migration Autor Nagarjun, A. Venkata, Rajkumar, Sujatha

“… Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis…”
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Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph Autor Tran, Quan M., Nguyen, Hien D., Huynh, Tai, Nguyen, Kha V., Hoang, Suong N., Pham, Vuong T.

“… Besides, an unsupervised deep learning model based on Variational Graph Autoencoder is also constructed to further learn and explore the behavior of users…”
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