Results 291 to 300 of about 4,772,239 (326)

ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation

International Conference on Information and Knowledge Management, 2022
Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems.
Yu Wang, Hengrui Zhang, Zhiwei Liu, Liangwei Yang, P. Yu   +4 more
semanticscholar   +1 more source

Fault diagnosis using variational autoencoder GAN and focal loss CNN under unbalanced data

Structural Health Monitoring
For the poor model generalization and low diagnostic efficiency of fault diagnosis under imbalanced distributions, a novel fault diagnosis method using variational autoencoder generation adversarial network and improved convolutional neural network ...
Weihan Li, Dunke Liu, Yang Li, Ming Hou, Jie Liu, Zhen Zhao, Aibin Guo, Huimin Zhao, Wu Deng   +8 more
semanticscholar   +1 more source

Unsupervised Health Indicator Construction by a Novel Degradation-Trend-Constrained Variational Autoencoder and Its Applications

IEEE/ASME transactions on mechatronics, 2022
Health indicator (HI) affects the accuracy and reliability of the remaining useful life (RUL) prediction model. The hidden variables of variational autoencoder (VAE) can represent the HI values for a life-cycle dataset with obvious degradation trend ...
Yi Qin, Jiang-Hong Zhou, Dingliang Chen
semanticscholar   +1 more source

Locational False Data Injection Attack Detection in Smart Grid Using Recursive Variational Graph Autoencoder

IEEE Internet of Things Journal
Stealthy false data injection attack (FDIA) that intentionally modifies measurement data of smart grid meters to bypass the traditional bad data detection module is one of menacing cyber attacks in smart grid. Due to requiring no costly labeling abnormal
Yu-Feng Wang, Ziyan Lu, Jian-Hua Ma, Qun Jin   +3 more
semanticscholar   +1 more source

A Semi-supervised Gaussian Mixture Variational Autoencoder method for few-shot fine-grained fault diagnosis

Neural Networks
In practical engineering, obtaining labeled high-quality fault samples poses challenges. Conventional fault diagnosis methods based on deep learning struggle to discern the underlying causes of mechanical faults from a fine-grained perspective, due to ...
Zhi-Qian Zhao, Ye-Yin Xu, Jiabin Zhang, R. Zhao, Zhao-Bo Chen, Y. Jiao   +5 more
semanticscholar   +1 more source

Multi-Modal Domain Adaptation Variational Autoencoder for EEG-Based Emotion Recognition

IEEE/CAA Journal of Automatica Sinica, 2022
Traditional electroencephalograph (EEG)-based emotion recognition requires a large number of calibration samples to build a model for a specific subject, which restricts the application of the affective brain computer interface (BCI) in practice.
Yixin Wang, Shuang Qiu, Dan Li, Changde Du, Bao-Liang Lu, Hui-Guang He   +5 more
semanticscholar   +1 more source

Mixture variational autoencoders

Pattern Recognition Letters, 2019
Abstract Variational autoencoders (VAEs) combine a generative model and a recognition model, and jointly train them to maximize a variational lower bound. VAEs play an important role in unsupervised learning and representation learning. But the isotropic generative model in VAEs cannot sufficiently utilize the latent representative space.
Shuoran Jiang, Yarui Chen, Jucheng Yang 0001, Chuanlei Zhang, Tingting Zhao 0001   +4 more
openaire   +1 more source

Variational Autoencoder for Anomaly Detection: A Comparative Study

arXiv.org
This paper aims to conduct a comparative analysis of contemporary Variational Autoencoder (VAE) architectures employed in anomaly detection, elucidating their performance and behavioral characteristics within this specific task.
H. Nguyen, C. Nguyễn, Xuan-Tung Dao, Quoc Trung Duong, Dzung Pham Thi Kim, Minh-Tan Pham   +5 more
semanticscholar   +1 more source

Personalized Federated Collaborative Filtering: A Variational AutoEncoder Approach

AAAI Conference on Artificial Intelligence
Federated Collaborative Filtering (FedCF) is an emerging field focused on developing a new recommendation framework with preserving privacy in a federated setting.
Zhiwei Li, Guodong Long, Tianyi Zhou, Jing Jiang, Cheng-Qi Zhang   +4 more
semanticscholar   +1 more source

Affine Variational Autoencoders

2019
Variational autoencoders (VAEs) have in recent years become one of the most powerful approaches to learning useful latent representations of data in an unsupervised manner. However, a major challenge with VAEs is that they have tremendous difficulty in generalizing to data that deviate from the training set (e.g., perturbed image variants).
Rene Bidart, Alexander Wong
openaire   +1 more source