Suchergebnisse - Graph conventional and variational autoencoder*

Multi-objective drug design with a scaffold-aware variational autoencoder von 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 …”
Volltext

Entropy-enhanced batch sampling and conformal learning in VGAE for physics-informed causal discovery and fault diagnosis von 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 …”
Volltext

Heterogeneous Hypergraph Variational Autoencoder for Link Prediction von 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 …”
Volltext

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction von 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 …”
Volltext

MODAPro: Explainable Heterogeneous Networks with Variational Graph Autoencoder for Mining Disease-Specific Functional Molecules and Pathways from Omics Data von 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 …”
Weitere Angaben

Anomaly Detection Based on Graph Convolutional Network–Variational Autoencoder Model Using Time-Series Vibration and Current Data von 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 …”
Volltext

Graph-Variational Convolutional Autoencoder-Based Fault Detection and Diagnosis for Photovoltaic Arrays von 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 …”
Volltext

GVDTI: graph convolutional and variational autoencoders with attribute-level attention for drug–protein interaction prediction von Hui Cui, Toshiya Nakaguchi, Ping Xuan, Mengsi Fan, Tiangang Zhang

Volltext

Self-Organizing Maps-Assisted Variational Autoencoder for Unsupervised Network Anomaly Detection von 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 …”
Volltext

Accelerating drug discovery targeting dihydroorotate dehydrogenase using machine learning and generative AI approaches von Krishnamurthy Ganga, Gayathri

“…  % on unseen molecules), demonstrating superior generalization. Using a Graph Convolutional Network-based Variational Autoencoder (GCN-VAE …”
Volltext

Automated site layout generation for buildings using graph constrained generative adversarial network von Jiang, Ming, Chen, Yimin, Liu, Xiaoping, Gao, Jinding

“… ), which consists of a graph variational autoencoder (GraphVAE) and a GAN framework. In this model, parcels are represented as tuples, while site layouts within each parcel …”
Volltext

Bridging VAE-Derived Latent Gene Representations and Graph Neural Networks for Improved Drug Response Prediction von Huang, Yen-Hua, Chiu, Yen-Jung

“… In this study, we develop a pharmacogenomics classification framework using Graph Convolutional Networks (GCNs …”
Volltext

Enhancing Facial Reconstruction Using Graph Attention Networks von 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 …”
Volltext

In-chip artificial intelligence technology for generating and self-correcting the topology of low-consumption RC filters von 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 …”
Volltext

Measuring the influence and amplification of users on social network with unsupervised behaviors learning and efficient interaction-based knowledge graph von 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 …”
Volltext

TV-CCANM: a transformer variational inference in confounding cascade additive noise model for causal effect estimation von Ahmad, Sohail, Wang, Hong

“… While the Confounding Cascade Nonlinear Additive Noise Model (CCANM) coupled with variational autoencoders (VAEs …”
Volltext

Open-world structured sequence learning via dense target encoding von 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 …”
Volltext

A densely connected framework for cancer subtype classification von 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 …”
Volltext

GraphTune: A Learning-Based Graph Generative Model With Tunable Structural Features von Watabe, Kohei, Nakazawa, Shohei, Sato, Yoshiki, Tsugawa, Sho, Nakagawa, Kenji

“… ) and a Conditional Variational AutoEncoder …”
Volltext

GraphTune: A Learning-based Graph Generative Model with Tunable Structural Features von Watabe, Kohei, Nakazawa, Shohei, Sato, Yoshiki, Tsugawa, Sho, Nakagawa, Kenji

“… ) and a Conditional Variational AutoEncoder …”
Volltext