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检索条件"机构=Center for Machine Intelligence and Data Science"
218 条 记 录,以下是1-10 订阅
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Intelligent Assistant for Multivariant Analysis  26
Intelligent Assistant for Multivariant Analysis
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26th International Conference of the Catalan Association for Artificial intelligence, CCIA 2024
作者: Angerri, Xavier Delgado, Oscar Gibert, Karina Knowledge Engineering and Machine Learning Group Intelligent Data Science and Artificial Intelligence Research Center Universtitat Politècnica de Catalunya Spain
When a Knowledge Discovery from data (KDD) (Fayyad, Piatetsky-Shapiro, & Smyth, 1996) process is being applied to get knowledge, several methods could be used (Gibert, et al., 2018). A simple and fast way to obtai... 详细信息
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Finding the transcription factor binding locations using novel algorithm segmentation to filtration (S2F)
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Journal of Ambient intelligence and Humanized Computing 2024年 第9期15卷 3347-3358页
作者: Theepalakshmi, P. Srinivasulu Reddy, U. Department of Computer Science and Engineering Gandhi Institute of Technology and Management Karnataka Bengaluru India Machine Learning and Data Analytics Lab Center of Excellence in Artificial Intelligence Department of Computer Applications National Institute of Technology Tamilnadu Tiruchirappalli India
The primary aim of identifying the binding motifs in gene regulation is to understand the transcriptional regulation molecular mechanism systematically. In this study, the (, d) motif search issue was considered ... 详细信息
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The Map Equation Goes Neural: Mapping Network Flows with Graph Neural Networks  38
The Map Equation Goes Neural: Mapping Network Flows with Gra...
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38th Conference on Neural Information Processing Systems, NeurIPS 2024
作者: Blöcker, Christopher Tan, Chester Scholtes, Ingo Data Analytics Group Department of Informatics University of Zurich Switzerland Machine Learning for Complex Networks Center for Artificial Intelligence and Data Science Julius-Maximilians-Universität Würzburg Germany
Community detection is an essential tool for unsupervised data exploration and revealing the organisational structure of networked systems. With a long history in network science, community detection typically relies ...
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Feature maps for the Laplacian kernel and its generalizations
arXiv
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arXiv 2025年
作者: Ahir, Sudhendu Pandit, Parthe Center for Machine Intelligence and Data Science IIT Bombay India
Recent applications of kernel methods in machine learning have seen a renewed interest in the Laplacian kernel, due to its stability to the bandwidth hyperparameter in comparison to the Gaussian kernel, as well as its... 详细信息
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Towards Highly Efficient Anomaly Detection for Predictive Maintenance  23
Towards Highly Efficient Anomaly Detection for Predictive Ma...
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23rd IEEE International Conference on machine Learning and Applications, ICMLA 2024
作者: Klüttermann, Simon Peka, Vanlal Doebler, Philipp Müller, Emmanuel Tu Dortmund University Dortmund Germany Lamarr Institute for Machine Learning and Artificial Intelligence Dortmund Germany Research Center Trustworthy Data Science and Security Dortmund Germany
This paper introduces SEAN, a novel anomaly detection algorithm designed for real-time applications in predictive maintenance. SEAN leverages an ensemble-based approach to deliver competitive performance while drastic... 详细信息
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Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling  23
Reinforcement Learning as an Improvement Heuristic for Real-...
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23rd IEEE International Conference on machine Learning and Applications, ICMLA 2024
作者: Müller, Arthur Vollenkemper, Lukas Fraunhofer IOSB-INA Department of Machine Intelligence Lemgo32657 Germany Center for Applied Data Science Bielefeld University of Applied Sciences and Arts Gütersloh33330 Germany
The integration of Reinforcement Learning (RL) with heuristic methods is an emerging trend for solving optimization problems, which leverages RL's ability to learn from the data generated during the search process... 详细信息
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Smaller Batches, Bigger Gains? Investigating the Impact of Batch Sizes on Reinforcement Learning Based Real-World Production Scheduling  29
Smaller Batches, Bigger Gains? Investigating the Impact of B...
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29th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2024
作者: Müller, Arthur Grumbach, Felix Sabatelli, Matthia Fraunhofer IOSB-INA Department of Machine Intelligence Lemgo32657 Germany Center for Applied Data Science Hochschule Bielefeld Gütersloh33330 Germany University of Groningen Department of Artificial Intelligence Groningen9712 CP Netherlands
Production scheduling is an essential task in manufacturing, with Reinforcement Learning (RL) emerging as a key solution. In a previous work, RL was utilized to solve an extended permutation flow shop scheduling probl... 详细信息
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GraphMorph: Tubular Structure Extraction by Morphing Predicted Graphs  38
GraphMorph: Tubular Structure Extraction by Morphing Predict...
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38th Conference on Neural Information Processing Systems, NeurIPS 2024
作者: Zhang, Zhao Zhao, Ziwei Wang, Dong Wang, Liwei Center for Data Science Peking University China Yizhun Medical AI Co. Ltd. China State Key Laboratory of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Center for Machine Learning Research Peking University China Guangdong Guangzhou China
Accurately restoring topology is both challenging and crucial in tubular structure extraction tasks, such as blood vessel segmentation and road network extraction. Diverging from traditional approaches based on pixel-...
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Online Query-Based data Pricing with Time-Discounting Valuations  40
Online Query-Based Data Pricing with Time-Discounting Valuat...
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40th IEEE International Conference on data Engineering, ICDE 2024
作者: Fu, Yicheng Miao, Xiaoye Peng, Huanhuan Na, Chongning Deng, Shuiguang Yin, Jianwei Zhejiang University Center for Data Science Hangzhou China Zhejiang University The State Key Lab of Brain-Machine Intelligence Hangzhou China PinTech Research Center Zhejiang Lab Hangzhou China College of Computer Science Zhejiang University Hangzhou China
Online data marketplaces emerge in diverse data-driven applications, where dynamically arriving consumers pur-chase the data at posted prices. The data value decays over time in many tasks, such as machine learning pr... 详细信息
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Optimal bounds for p sensitivity sampling via 2 augmentation  41
Optimal bounds for p sensitivity sampling via 2 augmentation
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41st International Conference on machine Learning, ICML 2024
作者: Munteanu, Alexander Omlor, Simon Dortmund Data Science Center Faculties of Statistics and Computer Science TU Dortmund University Dortmund Germany Faculty of Statistics TU Dortmund University Dortmund Germany Lamarr-Institute for Machine Learning and Artificial Intelligence Dortmund Germany
data subsampling is one of the most natural methods to approximate a massively large data set by a small representative proxy. In particular, sensitivity sampling received a lot of attention, which samples points prop... 详细信息
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