Search Results - conditional variational (autoencoder(cave)* OR autoencoder(cae)*)*

A-CAVE: Network abnormal traffic detection algorithm based on variational autoencoder

Authors: Shi Dong Huadong Su Yue Liu

Source: ICT Express, Vol 9, Iss 5, Pp 896-902 (2023)

Subject Terms: Conditional variational autoencoder, Anomaly traffic, Neural network, Intrusion detection, Information technology, T58.5-58.64

File Description: electronic resource

Relation: http://www.sciencedirect.com/science/article/pii/S2405959522001643; https://doaj.org/toc/2405-9595

Access URL: https://doaj.org/article/c7155765739a4d04bd6ef775b7e0d2e0

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder.

Authors: Wang, Xu Qian, Weiqi Zhao, Tun et al.

Source: Symmetry (20738994); Jul2024, Vol. 16 Issue 7, p791, 17p

Subject Terms: WIND tunnel testing, LATENT variables, REYNOLDS number, AEROFOILS, NETWORK performance

Anomaly Detection for Charging Piles Based on Conditional Variational Autoencoder.

Authors: Wang, Chuanjun Lu, Yinyu Wang, Mingxin et al.

Source: International Transactions on Electrical Energy Systems; 9/23/2025, Vol. 2025, p1-12, 12p

Subject Terms: ELECTRIC vehicles, SUPERVISED learning, PROBABILISTIC generative models, OUTLIER detection, SIGNAL detection, DISTRIBUTION (Probability theory), INFRASTRUCTURE (Economics)

STO-CVAE: state transition-oriented conditional variational autoencoder for data augmentation in disability classification.

Authors: Bang, Seong Jin Kang, Min Jung Lee, Min-Goo et al.

Source: Complex & Intelligent Systems; Jun2024, Vol. 10 Issue 3, p4201-4222, 22p

Subject Terms: HUMAN activity recognition, DATA augmentation, SUPPORT vector machines, HUMAN skeleton, ARTIFICIAL intelligence, MEDICAL rehabilitation

Topology Design of Soft Phononic Crystals for Tunable Band Gaps: A Deep Learning Approach.

Authors: Li, Jingru Qian, Minqi Yin, Jingming et al.

Source: Materials (1996-1944); Jan2025, Vol. 18 Issue 2, p377, 32p

Subject Terms: BAND gaps, PHONONIC crystals, DEEP learning, TRANSFER matrix, AUTOENCODERS

Diffangle-Grasp: Dexterous Grasp Synthesis via Fine-Grained Contact Generation and Natural Pose Optimization.

Authors: Ning, Meng Deng, Chong Zhan, Ziheng et al.

Source: Biomimetics (2313-7673); Aug2025, Vol. 10 Issue 8, p492, 20p

Subject Terms: PREHENSION (Physiology), HUMANOID robots, LATENT variables, POSTURE, STOCHASTIC models

多维度损失函数下引导词协同 sinkhorn 的监督式主题模型. (Chinese)

Alternate Title: Guided words and collaborative sinkhorn in supervised topic model under multi-dimensional loss function. (English)

Authors: 徐贞顺 王振彪 郑顺国 et al.

Source: Application Research of Computers / Jisuanji Yingyong Yanjiu; Sep2025, Vol. 42 Issue 9, p2644-2652, 9p

Subject Terms: LOSS functions (Statistics), OPTIMIZATION algorithms, INFORMATION processing, THEMATIC analysis

F0-consistent many-to-many non-parallel voice conversion via conditional autoencoder

Authors: Qian, Kaizhi Jin, Zeyu Hasegawa-Johnson, Mark et al.

Index Terms: Electrical Engineering and Systems Science - Audio and Speech Processing, Computer Science - Machine Learning, Computer Science - Sound, Statistics - Machine Learning, text

URL: http://arxiv.org/abs/2004.07370

Commonality Autoencoder: Learning Common Features for Change Detection From Heterogeneous Images.

Authors: Wu, Yue Li, Jiaheng Yuan, Yongzhe et al.

Source: IEEE Transactions on Neural Networks & Learning Systems; Sep2022, Vol. 33 Issue 9, p4257-4270, 14p

Subject Terms: SYNTHETIC apertures, ARTIFICIAL neural networks, SYNTHETIC aperture radar, FEATURE extraction, IMAGE representation

TRAFFIC CONTROL RECOGNITION WITH AN ATTENTION MECHANISM USING SPEED-PROFILE AND SATELLITE IMAGERY DATA.

Authors: Cheng, H. Lei, H. Zourlidou, S. et al.

Source: International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; 2022, Issue B4, p287-293, 7p

Subject Terms: REMOTE-sensing images, TRAFFIC engineering, ATTENTION control, DIGITAL maps, CONVOLUTIONAL neural networks, GEOSTATIONARY satellites, TRAFFIC signs & signals, TRAFFIC congestion

Geographic Terms: HANNOVER (Germany)

Reviews & Products: GOOGLE Maps

F0-Consistent Many-To-Many Non-Parallel Voice Conversion Via Conditional Autoencoder

Authors: Zeyu Jin Mark Hasegawa-Johnson Kaizhi Qian et al.

Source: ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :6284-6288

Subject Terms: FOS: Computer and information sciences, Computer Science - Machine Learning, Sound (cs.SD), Machine Learning (stat.ML), 02 engineering and technology, Computer Science - Sound, Machine Learning (cs.LG), 03 medical and health sciences, Statistics - Machine Learning, Audio and Speech Processing (eess.AS), FOS: Electrical engineering, electronic engineering, information engineering, 0202 electrical engineering, electronic engineering, information engineering, 0305 other medical science, Electrical Engineering and Systems Science - Audio and Speech Processing

Access URL: http://arxiv.org/pdf/2004.07370
http://arxiv.org/abs/2004.07370
https://www.arxiv-vanity.com/papers/2004.07370/
https://ui.adsabs.harvard.edu/abs/2020arXiv200407370Q/abstract
https://experts.illinois.edu/en/publications/f0-consistent-many-to-many-non-parallel-voice-conversion-via-cond
https://arxiv.org/pdf/2004.07370.pdf
https://arxiv.org/abs/2004.07370
https://ieeexplore.ieee.org/abstract/document/9054734

Attention-based framework for weakly supervised video anomaly detection.

Authors: Ma, Hualin Zhang, Liyan

Source: Journal of Supercomputing; Apr2022, Vol. 78 Issue 6, p8409-8429, 21p

Subject Terms: ANOMALY detection (Computer security), VIDEO surveillance, INTRUSION detection systems (Computer security), VIDEOS

Data-driven topology optimization using a multitask conditional variational autoencoder with persistent homology.

Authors: Sugai, Tomotaka Shintani, Kohei Yamada, Takayuki

Source: Structural & Multidisciplinary Optimization; Jul2024, Vol. 67 Issue 7, p1-12, 12p

Robust Zero-Day Attack Detection with Optimal Deep Learning for Securing Internet of Things Environment.

Authors: Abid, Nahla J. Alhebaishi, Nawaf Althaqafi, Turki

Source: Journal of Intelligent Systems & Internet of Things; 2025, Vol. 16 Issue 1, p118-131, 14p

Subject Terms: OPTIMIZATION algorithms, SOFTWARE maintenance, COMPUTER network security, DEEP learning, AUTOENCODERS

An efficient method for imbalanced fault diagnosis of rotating machinery.

Authors: Yang, Jingli Yin, Shuangyan Gao, Tianyu

Source: Measurement Science & Technology; Nov2021, Vol. 32 Issue 11, p1-15, 15p

Subject Terms: ROTATING machinery, GENERATIVE adversarial networks, FAULT diagnosis, PROBLEM solving

Autoencoder-Assisted Stacked Ensemble Learning for Lymphoma Subtype Classification: A Hybrid Deep Learning and Machine Learning Approach.

Authors: Ogundokun, Roseline Oluwaseun Owolawi, Pius Adewale Tu, Chunling et al.

Source: Tomography: A Journal for Imaging Research; Aug2025, Vol. 11 Issue 8, p91, 22p

Subject Terms: AUTOENCODERS, ENSEMBLE learning, DEEP learning, FEATURE extraction, CANCER diagnosis, HISTOPATHOLOGY, MACHINE learning

Disentangling genotype and environment specific latent features for improved trait prediction using a compositional autoencoder.

Authors: Powadi, Anirudha Jubery, Talukder Zaki Tross, Michael C. et al.

Source: Frontiers in Plant Science; 2024, p1-13, 13p

Subject Terms: LIFE sciences, PRINCIPAL components analysis, AUTOENCODERS, PLANT breeding, AGRICULTURE

Heartbeat detection and personal authentication using a 60 GHz Doppler sensor.

Authors: Asano, Takuma Izumi, Shintaro Kawaguchi, Hiroshi

Source: Frontiers in Digital Health; 2025, p1-12, 12p

Subject Terms: PATIENT monitoring equipment, BIOLOGICAL models, RESEARCH funding, SOUND spectrography, BIOMETRY, HEART beat, ELECTROCARDIOGRAPHY, SECURITY systems, TECHNOLOGY, PATIENT monitoring, MICROWAVES, WAVE analysis, ALGORITHMS

U-net like deep autoencoders for deblurring atmospheric turbulence.

Authors: Chen, Gongping Gao, Zhisheng Wang, Qiaolu et al.

Source: Journal of Electronic Imaging; Sep/Oct2019, Vol. 28 Issue 5, p1-14, 14p

Subject Terms: ATMOSPHERIC turbulence, ARTIFICIAL neural networks, IMAGE reconstruction, SIGNAL-to-noise ratio, FEATURE extraction, DATA integrity

Unsupervised detection of high-frequency oscillations in intracranial electroencephalogram: promoting a valuable automated diagnostic tool for epilepsy.

Authors: Chen, Wenjing Kang, Tongzhou Heyat, Md Belal Bin et al.

Source: Frontiers in Neurology; 2025, p1-11, 11p

Subject Terms: K-means clustering, WAVELET transforms, AUTOENCODERS, MACHINE learning, PEOPLE with epilepsy