Výsledky vyhledávání - "Computer Science: Machine Learning"

Disentangling Structured Components: Towards Adaptive, Interpretable and Scalable Time Series Forecasting Autor Jinliang Deng, Xiusi Chen, Renhe Jiang, null Du Yin, Yi Yang, Xuan Song, Ivor W. Tsang

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Moreau-Yoshida variational transport: a general framework for solving regularized distributional optimization problems Autor Nguyen, Dai Hai, Sakurai, Tetsuya

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Convergence Analysis of Mean Shift Autor Ryoya Yamasaki, Toshiyuki Tanaka

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A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers Autor Vinaytosh Mishra, Kishu Gupta, Deepika Saxena, Ashutosh Kumar Singh

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Boosting Simple Learners Autor Alon, Noga, Gonen, Alon, Hazan, Elad, Moran, Shay

“…Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate…”
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PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling With Masked Networks Autor Shunya Kato, Masaki Saito, Katsuhiko Ishiguro, Sol Cummings

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Machine Learning in High Energy Physics Community White Paper Autor Albertsson, Kim, Altoe, Piero, Anderson, Dustin, Andrews, Michael, Araque Espinosa, Juan Pedro, Aurisano, Adam, Basara, Laurent, Bevan, Adrian, Bonacorsi, Daniele, Campanelli, Mario, Capps, Louis, Carminati, Federico, Carrazza, Stefano, Childers, Taylor, Coniavitis, Elias, Cranmer, Kyle, David, Claire, Davis, Douglas, Duarte, Javier, Erdmann, Martin, Farbin, Amir, Feickert, Matthew, Castro, Nuno Filipe, Fitzpatrick, Conor, Forti, Alessandra, Garra-Tico, Jordi, Gemmler, Jochen, Girone, Maria, Glaysher, Paul, Gleyzer, Sergei, Gligorov, Vladimir, Golling, Tobias, Graw, Jonas, Gray, Lindsey, Hacker, Thomas, Hegner, Benedikt, Heinrich, Lukas, Hooberman, Ben, Kagan, Michael, Kane, Meghan, Kanishchev, Konstantin, Karpiński, Przemysław, Kassabov, Zahari, Kaul, Gaurav, Kcira, Dorian, Keck, Thomas, Klimentov, Alexei, Kurepin, Alexander, Kutschke, Rob, Kuznetsov, Valentin, Köhler, Nicolas, Lakomov, Igor, Lannon, Kevin, Lassnig, Mario, Limosani, Antonio, Louppe, Gilles, Mangu, Aashrita, Mato, Pere, Meinhard, Helge, Menasce, Dario, Moneta, Lorenzo, Narain, Meenakshi, Neubauer, Mark, Newman, Harvey, Pabst, Hans, Paganini, Michela, Paulini, Manfred, Perdue, Gabriel, Picazio, Attilio, Pivarski, Jim, Prosper, Harrison, Radovic, Alexander, Reece, Ryan, Rinkevicius, Aurelius, Rodrigues, Eduardo, Rorie, Jamal, Rousseau, David, Schramm, Steven, Schwartzman, Ariel, Severini, Horst, Seyfert, Paul, Siroky, Filip, Sokoloff, Mike, Stewart, Graeme, Stockdale, Ian, Strong, Giles, Thais, Savannah, Upfal, Eli, Usai, Emanuele, Ustyuzhanin, Andrey, Vallecorsa, Sofia, Vasel, Justin, Vilasís-Cardona, Xavier, Vlimant, Jean-Roch, Wang, Sean-Jiun, Watts, Gordon, Williams, Michael, Wu, Wenjing, Wunsch, Stefan, Zapata, Omar

“…Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s,…”
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Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach Autor Lyu, Yifeng, Hu, Han, Fan, Rongfei, Liu, Zhi, An, Jianping, Mao, Shiwen

“…The integrated satellite-terrestrial network (ISTN) system has experienced significant growth, offering seamless communication services in remote areas with…”
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Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning Autor Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin

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Exploring the Impact of Spatiotemporal Granularity on the Demand Prediction of Dynamic Ride-Hailing Autor Kai Liu, Zhiju Chen, Toshiyuki Yamamoto, Liheng Tuo

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On the Compressive Power of Boolean Threshold Autoencoders Autor Avraham A. Melkman, Sini Guo, Wai-Ki Ching, Pengyu Liu, Tatsuya Akutsu

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A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting Autor Jinliang Deng, Xiusi Chen, Renhe Jiang, Xuan Song, Ivor W. Tsang

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Self-Adaptive Training: Bridging Supervised and Self-Supervised Learning Autor Lang Huang, Chao Zhang, Hongyang Zhang

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Dynamic Transformation of Prior Knowledge Into Bayesian Models for Data Streams Autor Tran Xuan Bach, Nguyen Duc Anh, Ngo Van Linh, Khoat Than

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Divide-and-conquer based large-scale spectral clustering Autor Li, Hongmin, Ye, Xiucai, Imakura, Akira, Sakurai, Tetsuya

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A Method for Inferring Polymers Based on Linear Regression and Integer Programming Autor Ido, Ryota, Cao, Shengjuan, Zhu, Jianshen, Azam, Naveed Ahmed, Haraguchi, Kazuya, Zhao, Liang, Nagamochi, Hiroshi, Akutsu, Tatsuya

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Anoonly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies Autor Yixuan Zhou, Peiyu Yang, Yi Qu, Xing Xu, Zhe Sun, Andrzej Cichocki

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Addressing the Gap Between Training Data and Deployed Environment by On-Device Learning Autor Kazuki Sunaga, Masaaki Kondo, Hiroki Matsutani

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Theory-guided hard constraint projection (HCP): A knowledge-based data-driven scientific machine learning method Autor Dou Huang, Haoran Zhang, Jinyue Yan, Dongxiao Zhang, Nanzhe Wang, Junsheng Zeng, Yuntian Chen

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Binary classification with ambiguous training data Autor Otani Naoya, Yosuke Otsubo, Masashi Sugiyama, Tetsuya Koike

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