Search Results - "Graph Variational Autoencoder"

Transformer graph variational autoencoder for generative molecular design by Nguyen, Trieu, Karolak, Aleksandra

“… To address this, we present the transformer graph variational autoencoder (TGVAE), an innovative AI model that employs molecular graphs as input data, thus capturing…”
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Multiresolution equivariant graph variational autoencoder by Hy, Truong Son, Kondor, Risi

“…In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE…”
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Nearest Neighbours Graph Variational AutoEncoder by Arsini, Lorenzo, Caccia, Barbara, Ciardiello, Andrea, Giagu, Stefano, Mancini Terracciano, Carlo

“…Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems…”
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Graph variational autoencoder with affinity propagation for community-aware anomaly detection in attributed networks by Cao, Zhijie, Yang, Chengkun, Fan, Xiaoqing, Li, Lingjie, Lin, Qiuzhen, Li, Jianqiang, Ma, Lijia

“… community structures on ADAN. To address this issue, this paper proposes a novel framework called Graph Variational Autoencoder with Affinity Propagation (GVE-AP…”
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Adversarial regularize graph variational autoencoder based on encoder optimization strategy by Dai, Jin, Peng, Yanhui, Wang, Guoyin, Hu, Feng

“…Graph variational autoencoders (VAEs) have been widely used to address the representation problem of graph nodes…”
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Fragment‐based deep molecular generation using hierarchical chemical graph representation and multi‐resolution graph variational autoencoder by Gao, Zhenxiang, Wang, Xinyu, Blumenfeld Gaines, Blake, Shi, Xuetao, Bi, Jinbo, Song, Minghu

“…Graph generative models have recently emerged as an interesting approach to construct molecular structures atom‐by‐atom or fragment‐by‐fragment. In this study,…”
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A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network by Rico, Julian Carvajal, Alaeddini, Adel, Faruqui, Syed Hasib Akhter, Fisher-Hoch, Susan P, Mccormick, Joseph B

“… Our framework employs a graph variational autoencoder (GVAE) to capture the complex relationships in patient data…”
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MAGVA: An Open-Set Fault Diagnosis Model Based on Multi-Hop Attentive Graph Variational Autoencoder for Autonomous Vehicles by Fu, Rao, Bi, Yuanguo, Han, Guangjie, Zhang, Xiaoling, Liu, Li, Zhao, Liang, Hu, Bing

“… Motivated by these challenges, we propose a Multi-hop Attentive Graph Variational Autoencoder (MAGVA…”
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DAS-Accelerometer Data Fusion With Semi-Supervised Graph Variational Autoencoder for In-Service Train Wheel Flat Detection by Dong, Yiqing, Han, Chengjia, Qu, Shuai, Zhao, Chaoyang, Madan, Aayush, Fu, Yuguang, Yang, Yaowen

“…Wheel flats (WF) are a common defect in railway systems, posing risks to operational safety, passenger comfort, and the longevity of infrastructure. Existing…”
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Using Multi-Encoder Semi-Implicit Graph Variational Autoencoder to Analyze Single-Cell RNA Sequencing Data by Tian, Shengwen, Ji, Cunmei, Ni, Jiancheng, Wang, Yutian, Zheng, Chunhou

“…Rapid advances in single-cell RNA sequencing (scRNA-seq) have made it possible to characterize cell states at a high resolution view for large scale library…”
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VHGAE: Drug-Target Interaction Prediction Model Based on Heterogeneous Graph Variational Autoencoder by Zhang, Chen, Sun, Jiaqi, Xing, Linlin, Zhang, Longbo, Cai, Hongzhen, Che, Kai

“… the prediction performance of DTI. Therefore, we propose a method, called VHGAE, based on a heterogeneous graph variational autoencoder to predict drug-target interactions…”
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Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics by Paniagua, Karla, Huang, Yufei, Gao, Shou-jiang, Chen, Yidong, Jin, Yu-Fang, Flores, Mario

“…We present transformer and graph variational autoencoder to identify microenvironments (TG-ME…”
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Enhancing Gas Turbine Fault Diagnosis Using a Multi-Scale Dilated Graph Variational Autoencoder Model by Kun, Zhang, Hongren, Li, Xin, Wang, Daxing, Xie, Shuai, Yang

“…This paper proposes a Multi-scale Dilated Variational Graph Convolutional Autoencoder (MG-VAE) model for gas turbine fault diagnosis. The model integrates a…”
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piRNA-disease association prediction based on multi-channel graph variational autoencoder by Sun, Wei, Guo, Chang, Wan, Jing, Ren, Han

“… In this article, a novel computational method for predicting piRNA-disease associations using a multi-channel graph variational autoencoder (MC-GVAE) is proposed…”
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Malware detection framework based on graph variational autoencoder extracted embeddings from API-call graphs by Gunduz, Hakan

“… This study proposed a malware detection model based on API-call graphs and used Graph Variational Autoencoder (GVAE…”
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Disentangled Representation of Longitudinal Β-Amyloid for AD Via Sequential Graph Variational Autoencoder with Supervision by Yang, Fan, Wu, Guorong, Hwa Kim, Won

“…The emergence of Positron Emission Tomography (PET) imaging allows us to quantify the burden of amyloid plaques in-vivo, which is one of the hallmarks of…”
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Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics by Hariri, Ali, Dyachkova, Darya, Gleyzer, Sergei

“…Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with the detector…”
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Predictive Molecular Latent Space Discovery with Graph Variational Autoencoder by Shervani-Tabar, Navid

“…Recent advances in artificial intelligence have propelled the development of innovative computational materials modeling and design techniques. Generative deep…”
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Objective-directed deep graph generative model for automatic and intelligent highway interchange design by 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 by Tian, Mengxiao, Wu, Xinxiao, Jia, Yunde

“… Specifically, we use an improved graph variational autoencoder to separate the distracting factors and latent factor of relationships and jointly learn latent textual graph representations, latent…”
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