Search Results - "Variational autoencoder"

Crafting imperceptible on-manifold adversarial attacks for tabular data by 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 by 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 by 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 by 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 by 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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Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes by 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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Intrusion Detection System After Data Augmentation Schemes Based on the VAE and CVAE by 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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Spectrum-compatible artificial accelerograms via conditional variational autoencoder with generative adversarial networks by 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 by 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 by 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 by 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 by Ke, Qiao, Jing, Xinhui, Woźniak, Marcin, Xu, Shuang, Liang, Yunji, Zheng, Jiangbin

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Cloud-VAE: Variational autoencoder with concepts embedded by 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 by 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 by 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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Hyperbolic Adversarial Variational Embedding for item recommendation by 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 by 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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A generative design method of airfoil based on conditional variational autoencoder by 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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PFEMed: Few-shot medical image classification using prior guided feature enhancement by 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 by 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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