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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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.

“… Our framework integrates Variational Autoencoders (VAE) with Nonlinear Autoregressive with exogenous input (NARX…”
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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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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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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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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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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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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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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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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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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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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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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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A Zero-Shot Learning-Based Detection Model Against Zero-Day Attacks in IoT by Gao, Xueqin, Chen, Kai, Zhao, Yufei, Zhang, Peng, Han, Longxi, Zhang, Daojuan

“… Aiming at the problem of lack of available samples for zero-day attack detection in loT, we propose a zero-day attack detection method based on generative zero-shot learning…”
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Chaotic variational auto encoder-based adversarial machine learning by Pavan Venkata Sainadh Reddy, Daka, Vivek, Yelleti, Pranay, Gopi, Ravi, Vadlamani

“… This motivated us to propose a novel, computationally less expensive method for generating adversarial samples by employing a Variational Autoencoder (VAE…”
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Variational Structure Learning for Semi-Supervised Classification by Marthoglou, Konstantinos, Vretos, Nicholas, Daras, Petros

“… Graph structure learning or GSL can help in alleviating this problem, although many algorithms have shallow message propagation schemes, ignoring the deep expressibility that is possible with multi-layer networks…”
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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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