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检索条件"机构=Machine-Learning and Data Analytics"
243 条 记 录,以下是191-200 订阅
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Simple unsupervised keyphrase extraction using sentence embeddings  22
Simple unsupervised keyphrase extraction using sentence embe...
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22nd Conference on Computational Natural Language learning, CoNLL 2018
作者: Bennani-Smires, Kamil Musat, Claudiu Hossmann, Andreaa Baeriswyl, Michael Jaggi, Martin Data Analytics and AI Swisscom AG Switzerland Machine Learning and Optimization Laboratory EPFL Switzerland
Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Supervised keyphrase extraction requires large amounts of labeled training data and gen... 详细信息
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Artificial Intelligence for Imaging Diagnostics in Neurosurgery
Artificial Intelligence for Imaging Diagnostics in Neurosurg...
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2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
作者: Letyagin, Andrey Yu. Degtyareva, Liana O. Golushko, Sergey K. Rzaev, Jamil A. Amelin, Mihail E. Pavlovsky, Evgeniy N. Tuchinov, Bair N. Amelina, Evgeniya V. Moisak, Galina I. Bulgakova, Ekaterina G. Novosibirsk State University Novosibirsk Russia Federal State Budget Institution Federal Center of Neurosurgery Novosibirsk Russia FSBI 'Federal Neurosurgical Center' Novosibirsk Russia Stread Data Analytics Machine Learning Lab. Novosibirsk State University Novosibirsk Russia
Improving the accuracy and timeliness of medical imaging in everyday clinical neurosurgery practice is an urgent problem for the health care of all countries. This problem will be solved with the help of artificial in... 详细信息
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Rigid and non-rigid motion compensation in weight-bearing cone-beam CT of the knee using (noisy) inertial measurements
arXiv
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arXiv 2021年
作者: Maier, Jennifer Nitschke, Marlies Choi, Jang-Hwan Gold, Garry Fahrig, Rebecca Eskofier, Bjoern M. Maier, Andreas Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Machine Learning and Data Analytics Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Division of Mechanical and Biomedical Engineering Ewha Womans University Seoul Korea Republic of Department of Radiology School of Medicine Stanford University StanfordCA United States Innovation Advanced Therapies Siemens Healthcare GmbH Forchheim Germany
Involuntary subject motion is the main source of artifacts in weight-bearing cone-beam CT of the knee. To achieve image quality for clinical diagnosis, the motion needs to be compensated. We propose to use inertial me... 详细信息
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Reducing over-smoothness in speech synthesis using Generative Adversarial Networks
Reducing over-smoothness in speech synthesis using Generativ...
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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON
作者: Leyuan Sheng Evgeniy N. Pavlovskiy Novosibirsk State University Novosibirsk Russia Stream Data Analytics and Machine Learning laboratory Novosibirsk State University Novosibirsk Russia
Speech synthesis is widely used in many practical applications. In recent years, speech synthesis technology has developed rapidly. However, one of the reasons why synthetic speech is unnatural is that it often has ov... 详细信息
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Uncertainty Quantification in machine learning for Engineering Design and Health Prognostics: A Tutorial
arXiv
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arXiv 2023年
作者: Nemani, Venkat Biggio, Luca Huan, Xun Hu, Zhen Fink, Olga Tran, Anh Wang, Yan Zhang, Xiaoge Hu, Chao Department of Mechanical Engineering Iowa State University AmesIA50011 United States Data Analytics Lab ETH Zürich Switzerland Department of Mechanical Engineering University of Michigan Ann ArborMI48109 United States Department of Industrial and Manufacturing Systems Engineering University of Michigan-Dearborn DearbornMI48128 United States Intelligent Maintenance and Operations Systems EPFL Lausanne12309 Switzerland Scientific Machine Learning Sandia National Laboratories AlbuquerqueNM87123 United States George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Kowloon Hong Kong New Territories Hong Kong Department of Mechanical Engineering University of Connecticut StorrsCT06269 United States
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and mana... 详细信息
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Multidimensional scaling, Sammon mapping, and Isomap: Tutorial and survey
arXiv
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arXiv 2020年
作者: Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada
Multidimensional Scaling (MDS) is one of the first fundamental manifold learning methods. It can be categorized into several methods, i.e., classical MDS, kernel classical MDS, metric MDS, and non-metric MDS. Sammon m... 详细信息
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Stochastic Neighbor Embedding with Gaussian and Student-t Distributions: Tutorial and Survey
arXiv
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arXiv 2020年
作者: Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada
Stochastic Neighbor Embedding (SNE) is a manifold learning and dimensionality reduction method with a probabilistic approach. In SNE, every point is consider to be the neighbor of all other points with some probabilit... 详细信息
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Locally linear embedding and its variants: Tutorial and survey
arXiv
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arXiv 2020年
作者: Ghojogh, Benyamin Ghodsi, Ali Karray, Fakhri Crowley, Mark Department of Electrical and Computer Engineering Machine Learning Laboratory University of Waterloo WaterlooON Canada Department of Statistics and Actuarial Science & David R. Cheriton School of Computer Science Data Analytics Laboratory University of Waterloo WaterlooON Canada Department of Electrical and Computer Engineering Centre for Pattern Analysis and Machine Intelligence University of Waterloo WaterlooON Canada
This is a tutorial and survey paper for Locally Linear Embedding (LLE) and its variants. The idea of LLE is fitting the local structure of manifold in the embedding space. In this paper, we first cover LLE, kernel LLE... 详细信息
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Harnessing multimodal approaches for depression detection using large language models and facial expressions
Npj mental health research
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Npj mental health research 2024年 第1期3卷 66页
作者: Misha Sadeghi Robert Richer Bernhard Egger Lena Schindler-Gmelch Lydia Helene Rupp Farnaz Rahimi Matthias Berking Bjoern M Eskofier Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. misha.sadeghi@fau.de. Machine Learning and Data Analytics Lab (MaD Lab) Department Artificial Intelligence in Biomedical Engineering (AIBE) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Chair of Visual Computing (LGDV) Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91058 Germany. Chair of Clinical Psychology and Psychotherapy (KliPs) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen 91052 Germany. Translational Digital Health Group Institute of AI for Health Helmholtz Zentrum München - German Research Center for Environmental Health Neuherberg 85764 Germany.
Detecting depression is a critical component of mental health diagnosis, and accurate assessment is essential for effective treatment. This study introduces a novel, fully automated approach to predicting depression s...
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learning with Group Noise
arXiv
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arXiv 2021年
作者: Wang, Qizhou Yao, Jiangchao Gong, Chen Liu, Tongliang Gong, Mingming Yang, Hongxia Han, Bo Department of Computer Science Hong Kong Baptist University Hong Kong Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of MoE School of Computer Science and Engineering Nanjing University of Science and Technology China Data Analytics and Intelligence Lab Alibaba Group China Department of Computing Hong Kong Polytechnic University Hong Kong Trustworthy Machine Learning Lab School of Computer Science University of Sydney Australia School of Mathematics and Statistics University of Melbourne Australia
machine learning in the context of noise is a challenging but practical setting to plenty of real-world applications. Most of the previous approaches in this area focus on the pairwise relation (casual or correlationa... 详细信息
来源: 评论