Search Results - We propose a multi-layer variational autoencoder method*

Hierarchical Residual Learning Based Vector Quantized Variational Autoencoder for Image Reconstruction and Generation by Adiban, Mohammad, Stefanov, Kalin, Sabato Marco Siniscalchi, Salvi, Giampiero

“…We propose a multi-layer variational autoencoder method, we call HR-VQVAE, that learns hierarchical discrete representations of the data…”
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Building damage detection from multi-feature fusion of Sentinel-1/2 imagery using variational autoencoder and MLP-Mixer network: insights from the Jishishan earthquake by Jing Wang, Xiwei Fan, Yuanmeng Qi, Wenyu Nie, Jiwen An, Xuefei Zhang, Hua Wu

“… The approach integrates a Variational Autoencoder (VAE) with an improved MLP-Mixer network to extract compact, discriminative representations…”
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Deep Learning Approach for Epileptic Focus Localization by Daoud, Hisham, Bayoumi, Magdy

“… Our first proposed method is based on semi-supervised learning, in which a deep convolutional autoencoder is trained and then the pre-trained encoder is used with multi-layer perceptron as a classifier…”
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Deep Learning for Identifying Promising Drug Candidates in Drug–Phospholipid Complexes by Yoo, Soyoung, Lee, Hanbyul, Kim, Junghyun

“…Drug–phospholipid complexing is a promising formulation technology for improving the low bioavailability of active pharmaceutical ingredients (APIs). However,…”
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Drug repositioning based on heterogeneous networks and variational graph autoencoders by Lei, Song, Lei, Xiujuan, Liu, Lian

“… years has facilitated drug development. In this study we propose a drug repositioning method, VGAEDR, based on a heterogeneous network of multiple drug attributes and a variational graph autoencoder…”
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Nonlinear system identification using modified variational autoencoders by Paniagua, Jose L., López, Jesús A.

“…This research proposes a methodology for identifying nonlinear systems using input/output data and deep learning generative models…”
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The Wyner Variational Autoencoder for Unsupervised Multi-Layer Wireless Fingerprinting by Teng-Hui, Huang, Dahanayaka, Thilini, Thilakarathna, Kanchana, Leong, Philip H W, Hesham El Gamal

“…, packet length, without decryption of the payload. Inspired by these results, we propose a multi-layer fingerprinting framework that jointly considers the multi-layer signatures for improved identification performance…”
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Remaining Useful Life Prediction Method Based on Conv-Transformer Variational Autoencoder by Wang, Junjie, Hu, Xiaofeng, Yang, Yuwang

“… To address these, this paper proposes a new RUL prediction method based on Conv-Transformer Variational Autoencoder…”
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APGVAE: Adaptive disentangled representation learning with the graph-based structure information by Ke, Qiao, Jing, Xinhui, Woźniak, Marcin, Xu, Shuang, Liang, Yunji, Zheng, Jiangbin

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Data augmentation with norm-AE and selective pseudo-labelling for unsupervised domain adaptation by Wang, Qian, Meng, Fanlin, Breckon, Toby P.

“…, a shallow Multi-Layer Perceptron) trained in the original feature space. Besides, we propose a novel generative model norm…”
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Enhancing microbe-disease association prediction via multi-view graph convolution and latent feature learning by Wang, Bo, Wu, Peilong, Du, Xiaoxin, Zhang, Chunyu, Fu, Shanshan, Sun, Tang, Yang, Xue

“…), variational autoencoders (VAEs), and dynamic kernel matrix weighting for microbe-disease association (MDA) prediction…”
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Securing Multi-Layer Federated Learning: Detecting and Mitigating Adversarial Attacks by Gouge, Justin, Wang, Ping

“… In this work, we propose new methods for anomaly detection and removal of attackers from training in a multi-layer FL system…”
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SVAE‐GRU‐based degradation generation and prediction for small samples by Shangguan, Anqi, Feng, Nan, Mu, Lingxia, Fei, Rong, Hei, Xinhong, Xie, Guo

“… New degradation data are generated by combining the Stacked Variational Autoencoder (SVAE…”
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Soft Sensor Development Based on Deep Extended Variational Autoencoder with Just-in-time Learning by Shengjie, Xiong, Li, Xie, Liang, Xu

“… To address this issue, this paper proposes a soft sensor development method based on a deep extended variational autoencoder with just-in-Time Learning (JITL-DE-VAE…”
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Quantum deep learning-enhanced ethereum blockchain for cloud security: intrusion detection, fraud prevention, and secure data migration by Nagarjun, A. Venkata, Rajkumar, Sujatha

“… Conventional blockchain security methods suffer from poor scalability and dynamic threat analysis…”
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Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations by Zhang, Yan, Li, Changyu, Tsang, Ivor W., Xu, Hui, Duan, Lixin, Yin, Hongzhi, Li, Wen, Shao, Jie

“…Cold-start issues have been more and more challenging for providing accurate recommendations with the fast increase of users and items. Most existing…”
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HetFCM: functional co-module discovery by heterogeneous network co-clustering by Tan, Haojiang, Guo, Maozu, Chen, Jian, Wang, Jun, Yu, Guoxian

“…) to detect functional co-modules. HetFCM introduces an attributed heterogeneous network to jointly model interplays and multi-type attributes of different molecules, and applies multiple variational graph autoencoders on the network…”
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CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data by Liu, Wei, Teng, Zhijie, Li, Zejun, Chen, Jing

“… is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE…”
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Anomaly Detection of Acoustic Signals in Ultra-High Voltage Converter Valves Based on the FAVAE-AS by Pan, Shuyan, Tang, Mingzhu, Li, Na, Zuo, Jiawen, Zhou, Xingpeng

“… It extracts probability features via a conditional variational autoencoder, strengthens feature representation using multi-layer convolution and residual…”
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Simple and Effective Graph Autoencoders with One-Hop Linear Models by Salha, Guillaume, Hennequin, Romain, Vazirgiannis, Michalis

“…Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering…”
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