Suchergebnisse - "Variational AutoEncoder"

Crafting imperceptible on-manifold adversarial attacks for tabular data von He, Zhipeng, Stevens, Alexander, Ouyang, Chun, De Smedt, Johannes, Barros, Alistair, Moreira, Catarina

“… Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where …”
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Anomaly detection for multivariate times series through the multi-scale convolutional recurrent variational autoencoder von Xie, Tianming, Xu, Qifa, Jiang, Cuixia

“… To realize the anomaly detection for industrial multi-sensor data, we develop a novel multi-scale convolutional recurrent variational autoencoder (MSCRVAE) …”
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Robust Semantic Communications with Masked VQ-VAE Enabled Codebook von Hu, Qiyu, Zhang, Guangyi, Qin, Zhijin, Cai, Yunlong, Yu, Guanding, Li, Geoffrey Ye

“… Although semantic communications have exhibited satisfactory performance on a large number of tasks, the impact of semantic noise and the robustness of the …”
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VAE-based Deep SVDD for anomaly detection von Zhou, Yu, Liang, Xiaomin, Zhang, Wei, Zhang, Linrang, Song, Xing

“… Anomaly detection is an essential task for different fields in the real world. The imbalanced data and lack of labels make the task challenging. Deep learning …”
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Recognition of geochemical anomalies using a deep variational autoencoder network von Luo, Zijing, Xiong, Yihui, Zuo, Renguang

“… Deep learning (DL) algorithms have received increased attention in various fields. In the field of geoscience, DL has been shown to be a powerful tool for …”
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Intrusion Detection System After Data Augmentation Schemes Based on the VAE and CVAE von Liu, Chang, Antypenko, Ruslan, Sushko, Iryna, Zakharchenko, Oksana

“… Industrial Internet of Things (IoT) is the most rapidly developing industry in the current IoT industry, and the intrusion detection system (IDS) remains one …”
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Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes von Kebaili, Aghiles, Lapuyade-Lahorgue, Jérôme, Vera, Pierre, Ruan, Su

“… Deep learning has gained significant attention in medical image segmentation. However, the limited availability of annotated training data presents a challenge …”
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Spectrum-compatible artificial accelerograms via conditional variational autoencoder with generative adversarial networks von Hu, Xiaohu, Chen, Su, Ding, Yi, Fu, Lei, Li, Xiaojun

“… The need for spectrum-compatible ground motions in structural seismic design has driven the development of artificial seismic waveform generation techniques …”
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Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study von Zeroual, Abdelhafid, Harrou, Fouzi, Dairi, Abdelkader, Sun, Ying

“… •Developed deep learning methods to forecast the COVID19 spread.•Five deep learning models have been compared for COVID-19 forecasting.•Time-series COVID19 …”
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Deep Clustering Analysis via Dual Variational Autoencoder With Spherical Latent Embeddings von Yang, Lin, Fan, Wentao, Bouguila, Nizar

“… In recent years, clustering methods based on deep generative models have received great attention in various unsupervised applications, due to their …”
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Intelligent Condition-Based Monitoring of Rotary Machines With Few Samples von Dixit, Sonal, Verma, Nishchal K.

“… Recently, intelligent condition based monitoring systems build on deep learning methods have gained popularity. The success of these methods relies upon the …”
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APGVAE: Adaptive disentangled representation learning with the graph-based structure information von Ke, Qiao, Jing, Xinhui, Woźniak, Marcin, Xu, Shuang, Liang, Yunji, Zheng, Jiangbin

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Cloud-VAE: Variational autoencoder with concepts embedded von Liu, Yue, Liu, Zitu, Li, Shuang, Yu, Zhenyao, Guo, Yike, Liu, Qun, Wang, Guoyin

“… •The initial concepts in latent space are described as prior distribution obtained by the proposed cloud model-based clustering algorithm.•Variational lower …”
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Uncertainty-aware probabilistic travel demand prediction for mobility-on-demand services von Peng, Tao, Gao, Jie, Cats, Oded

“… •Spatial-temporal deep learning framework for probabilistic MoD demand prediction.•Nonparametric approach for uncertainty quantification in travel demand …”
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A review of molecular representation in the age of machine learning von Wigh, Daniel S., Goodman, Jonathan M., Lapkin, Alexei A.

“… Research in chemistry increasingly requires interdisciplinary work prompted by, among other things, advances in computing, machine learning, and artificial …”
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A generative design method of airfoil based on conditional variational autoencoder von Wang, Xu, Qian, Weiqi, Zhao, Tun, Chen, Hai, He, Lei, Sun, Haisheng, Tian, Yuan

“… The challenges in multi-objective and multi-dimensional optimization design of airfoils, marked by prolonged optimization cycles and low accuracy, call for an …”
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Hyperbolic Adversarial Variational Embedding for item recommendation von Sun, Zhongchuan, Chen, Liming, Wang, Youwei, Zhang, Mingming, Wu, Yunpeng, Ye, Yangdong

“… Variational autoencoders (VAEs) have shown great promise in recommender systems due to their advantage of handling implicit feedback. However, existing …”
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DiffuseVAE++: Mitigating training-sampling mismatch based on additional noise for higher fidelity image generation von Yang, Xiaobao, Luo, Wei, Ning, Hailong, Zhang, Guorui, Sun, Wei, Ma, Sugang

“… Denoising Diffusion Probabilistic Models (DDPMs) have demonstrated remarkable results in image generation. However, there exist a mismatch between the training …”
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PFEMed: Few-shot medical image classification using prior guided feature enhancement von Dai, Zhiyong, Yi, Jianjun, Yan, Lei, Xu, Qingwen, Hu, Liang, Zhang, Qi, Li, Jiahui, Wang, Guoqiang

“… •A novel dual-encoder architecture is introduced to extract feature representation.•To our knowledge, we are the first to investigate the proposed VAE …”
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Joint Coding-Modulation for Digital Semantic Communications via Variational Autoencoder von Bo, Yufei, Duan, Yiheng, Shao, Shuo, Tao, Meixia

“… Semantic communications have emerged as a new paradigm for improving communication efficiency by transmitting the semantic information of a source message that …”
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