Suchergebnisse - (conditional OR conditioning) variational (autoencoder(cave) OR autoencoder(cae))

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

Autoren: Shi Dong Huadong Su Yue Liu

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

Schlagwörter: Conditional variational autoencoder, 0202 electrical engineering, electronic engineering, information engineering, Intrusion detection, Information technology, 02 engineering and technology, T58.5-58.64, Anomaly traffic, Neural network

Zugangs-URL: https://doaj.org/article/c7155765739a4d04bd6ef775b7e0d2e0

Anomaly Detection for Charging Piles Based on Conditional Variational Autoencoder.

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

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

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

Dall-e Brain : Generation of 2D T1-w, T2-w, and FLAIR MRI images using a prompt

Autoren: El-Allaly, Souhail Breuil, Julie Bibard-Grau, Emma et al.

Weitere Verfasser: El-Allaly, Souhail Breuil, Julie Bibard-Grau, Emma et al.

Quelle: 3e édition du Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM 2025).
https://hal.science/hal-05013997
3e édition du Colloque Français d'Intelligence Artificielle en Imagerie Biomédicale (IABM 2025)., Mar 2025, Nice, France

Schlagwörter: Registration, Image Encoder/Decoder, Variational AutoEncoder, Latent Diffusion Model, Text prompt conditioning, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], [INFO.INFO-IM]Computer Science [cs]/Medical Imaging

Geographisches Schlagwort: Nice, France

Time: Nice, France

Verfügbarkeit: https://hal.science/hal-05013997
https://hal.science/hal-05013997v1/document
https://hal.science/hal-05013997v1/file/Poster_Dalle_Brain_IABM_TG_SE_vfi_3.pdf

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Schlagwörter: BAND gaps, PHONONIC crystals, DEEP learning, TRANSFER matrix, AUTOENCODERS

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

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

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

Schlagwörter: LIFE sciences, PRINCIPAL components analysis, AUTOENCODERS, PLANT breeding, AGRICULTURE

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

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

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

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

Analyzing social psychological impact on emotional expression through peer communication using crayfish optimization algorithm with deep learning model.

Autoren: Alzubaidi, Umkalthoom

Quelle: Scientific Reports; 7/19/2025, Vol. 15 Issue 1, p1-15, 15p

Schlagwörter: SOCIAL psychology, HUMAN-computer interaction, PEER communication, NATURAL language processing, SENTIMENT analysis, OPTIMIZATION algorithms, FACIAL expression & emotions (Psychology), DEEP learning

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)

Autoren: 徐贞顺 王振彪 郑顺国 et al.

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

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

Parallel Interaction Spatiotemporal Constrained Variational Autoencoder for Soft Sensor Modeling.

Autoren: Zhu, Xiuli Damarla, Seshu Kumar Hao, Kuangrong et al.

Quelle: IEEE Transactions on Industrial Informatics; Aug2022, Vol. 18 Issue 8, p5190-5198, 9p

A Variational Autoencoder Cascade Generative Adversarial Network for Scalable 3D Object Generation and Reconstruction.

Autoren: Yu, Min-Su Jung, Tae-Won Yun, Dai-Yeol et al.

Quelle: Sensors (14248220); Feb2024, Vol. 24 Issue 3, p751, 17p

Schlagwörter: GENERATIVE adversarial networks, SHARED virtual environments, VIRTUAL reality

Pythia: An Edge-First Agent for State Prediction in High-Dimensional Environments.

Autoren: Karatzas, Andreas Anagnostopoulos, Iraklis

Quelle: IEEE Embedded Systems Letters; Dec2024, Vol. 16 Issue 4, p473-476, 4p

Context Autoencoder for Self-supervised Representation Learning.

Autoren: Chen, Xiaokang Ding, Mingyu Wang, Xiaodi et al.

Quelle: International Journal of Computer Vision; Jan2024, Vol. 132 Issue 1, p208-223, 16p

Schlagwörter: DEEP learning, TASK performance

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

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

Index Begriffe: 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

Dragon_Pi: IoT Side-Channel Power Data Intrusion Detection Dataset and Unsupervised Convolutional Autoencoder for Intrusion Detection.

Autoren: Lightbody, Dominic Ngo, Duc-Minh Temko, Andriy et al.

Quelle: Future Internet; Mar2024, Vol. 16 Issue 3, p88, 38p

Schlagwörter: INTRUSION detection systems (Computer security), COMPUTER network traffic, INTERNET of things, CYBERTERRORISM, DRAGONS, ARTIFICIAL intelligence

Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis.

Autoren: Flores, Mario Liu, Zhentao Zhang, Tinghe et al.

Quelle: Briefings in Bioinformatics; Jan2022, Vol. 23 Issue 1, p1-31, 31p

Schlagwörter: DEEP learning, GENERATIVE adversarial networks, MACHINE learning, ELECTRONIC data processing

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

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

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

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