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检索条件"主题词=Variational autoencoder"
1535 条 记 录,以下是1421-1430 订阅
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Improved history matching of channelized reservoirs using a novel deep learning-based parametrization method
GEOENERGY SCIENCE AND ENGINEERING
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GEOENERGY SCIENCE AND ENGINEERING 2023年 229卷
作者: Yousefzadeh, Reza Ahmadi, Mohammad Amirkabir Univ Technol Dept Petr Engn Tehran Iran
Most of the geological parametrization techniques used in history matching of sub-surface formations including the deep learning-based methods could not capture the non-linear and non-Gaussian dependencies and were li... 详细信息
来源: 评论
AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design
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COMPUTATIONAL BIOLOGY AND CHEMISTRY 2023年 107卷 107971页
作者: Wang, Mengying Li, Weimin Yu, Xiao Luo, Yin Han, Ke Wang, Can Jin, Qun Shanghai Univ Sch Comp Engn & Sci Shanghai Peoples R China East China Normal Univ Sch Life Sci Shanghai Peoples R China Liaocheng Peoples Hosp Med & Hlth Ctr Liaocheng Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Australia Waseda Univ Networked Informat Syst Lab Tokyo Japan
In the prediction of protein-ligand affinity, the traditional methods require a large amount of computing resources, and have certain limitations in predicting and simulating the structural changes. Although employing... 详细信息
来源: 评论
Deep multi-sphere support vector data description based on disentangled representation learning
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PATTERN RECOGNITION 2024年 156卷
作者: Xing, Hong-Jie Wu, Hui-Nan Zhang, Ping-Ping Hebei Univ Sch Cyber Secur & Comp Baoding 071000 Peoples R China Hebei Univ Sch Math & Informat Sci Hebei Key Lab Machine Learning & Computat Intellig Baoding 071002 Peoples R China Hebei Meteorol Bur Hebei Meteorol Informat Ctr Shijiazhuang 050021 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China
Deep support vector data description (Deep SVDD) combines deep mapping network and support vector data description (SVDD) to jointly optimize network connection weights and hypersphere volume. However, when the parame... 详细信息
来源: 评论
Data-driven multifidelity topology design using a deep generative model: Application to forced convection heat transfer problems
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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2022年 388卷 114284-114284页
作者: Yaji, Kentaro Yamasaki, Shintaro Fujita, Kikuo Osaka Univ Grad Sch Engn Dept Mech Engn 2-1 Yamadaoka Suita Osaka 5650871 Japan
Topology optimization is a powerful methodology for generating novel designs with a high degree of design freedom. In exchange for this attractive feature, topology optimization cannot generally avoid multimodality, w... 详细信息
来源: 评论
VASP: An autoencoder-based approach for multivariate anomaly detection and robust time series prediction with application in motorsport
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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 104卷 104354-104354页
作者: von Schleinitz, Julian Graf, Michael Trutschnig, Wolfgang Schroeder, Andreas BMW AG Motorsport Munich Germany Univ Salzburg Salzburg Austria
The aim is to provide a framework for robust time series prediction in the presence of anomalies. The framework is developed based on a data set from motorsport but is not limited to this specific area. In motorsport,... 详细信息
来源: 评论
A survey on neural topic models: methods, applications, and challenges
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ARTIFICIAL INTELLIGENCE REVIEW 2024年 第2期57卷 18-18页
作者: Wu, Xiaobao Nguyen, Thong Luu, Anh Tuan Nanyang Technol Univ Sch Comp Sci & Engn 50 Nanyang Ave Singapore 639798 Singapore Natl Univ Singapore Sch Comp 21 Lower Kent Ridge Rd Singapore 119077 Singapore
Topic models have been prevalent for decades to discover latent topics and infer topic proportions of documents in an unsupervised fashion. They have been widely used in various applications like text analysis and con... 详细信息
来源: 评论
PCGen: A Fully Parallelizable Point Cloud Generative Model
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SENSORS 2024年 第5期24卷 1414页
作者: Vercheval, Nicolas Royen, Remco Munteanu, Adrian Pizurica, Aleksandra Univ Ghent Fac Engn & Architecture Dept Telecommun & Informat Proc Res Grp Artificial Intelligence & Sparse Modelling B-9000 Ghent Belgium Univ Ghent Fac Engn & Architecture Dept Elect & Informat Syst Clifford Res Grp B-9000 Ghent Belgium Vrije Univ Brussel Dept Elect & Informat ETRO Fac Engn B-1050 Brussels Belgium
Generative models have the potential to revolutionize 3D extended reality. A primary obstacle is that augmented and virtual reality need real-time computing. Current state-of-the-art point cloud random generation meth... 详细信息
来源: 评论
Disentangling the correlated continuous and discrete generative factors of data
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PATTERN RECOGNITION 2023年 133卷
作者: Choi, Jaewoong Hwang, Geonho Kang, Myungjoo Seoul Natl Univ Dept Math Sci 1 Gwanak Ro Seoul 08826 South Korea Korea Inst Adv Study KIAS Ctr Artificial Intelligence & Nat Sci Seoul South Korea
Real-world data typically include discrete generative factors, such as category labels and the existence of objects, as well as continuous generative factors. Continuous generative factors may be dependent on or indep... 详细信息
来源: 评论
An Auto-Encoder with Genetic Algorithm for High Dimensional Data: Towards Accurate and Interpretable Outlier Detection
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ALGORITHMS 2022年 第11期15卷 429-429页
作者: Li, Jiamu Zhang, Ji Bah, Mohamed Jaward Wang, Jian Zhu, Youwen Yang, Gaoming Li, Lingling Zhang, Kexin Nanjing Univ Aeronaut & Astronaut Sch Comp Sci & Technol Nanjing 210016 Peoples R China Univ Southern Queensland Sch Math Phys & Comp Toowoomba Qld 4350 Australia Zhejiang Lab Big Data Intelligence Res Ctr Hangzhou 311121 Peoples R China Anhui Univ Sci & Technol Sch Comp Sci & Engn Huainan 243002 Peoples R China Zhengzhou Univ Aeronaut Sch Intelligent Engn Zhengzhou 450046 Peoples R China
When dealing with high-dimensional data, such as in biometric, e-commerce, or industrial applications, it is extremely hard to capture the abnormalities in full space due to the curse of dimensionality. Furthermore, i... 详细信息
来源: 评论
Enhancing biomechanical machine learning with limited data: generating realistic synthetic posture data using generative artificial intelligence
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FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY 2024年 12卷 1350135页
作者: Dindorf, Carlo Dully, Jonas Konradi, Juergen Wolf, Claudia Becker, Stephan Simon, Steven Huthwelker, Janine Werthmann, Frederike Kniepert, Johanna Drees, Philipp Betz, Ulrich Froehlich, Michael Univ Kaiserslautern Landau Dept Sports Sci Kaiserslautern Germany Johannes Gutenberg Univ Mainz Univ Med Ctr Inst Phys Therapy Prevent & Rehabil Mainz Germany Johannes Gutenberg Univ Mainz Univ Med Ctr Dept Orthoped & Trauma Surg Mainz Germany
Objective: Biomechanical Machine Learning (ML) models, particularly deep-learning models, demonstrate the best performance when trained using extensive datasets. However, biomechanical data are frequently limited due ... 详细信息
来源: 评论