Suchergebnisse - "Computer Science - Machine Learning"

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

Volltext

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

Volltext

Convergence Analysis of Mean Shift von Ryoya Yamasaki, Toshiyuki Tanaka

Volltext

Dataset Shift in Machine Learning von Quinonero-Candela, Joaquin, Sugiyama, Masashi, Schwaighofer, Anton, Lawrence, Neil D

“… Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test …”
Volltext

Distributional reinforcement learning von Bellemare, Marc G, Dabney, Will, Rowland, Mark

“… The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a …”
Volltext

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

Volltext

Boosting Simple Learners von 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 …”
Volltext

PolMERLIN: Self-Supervised Polarimetric Complex SAR Image Despeckling With Masked Networks von Shunya Kato, Masaki Saito, Katsuhiko Ishiguro, Sol Cummings

Volltext

Machine Learning in High Energy Physics Community White Paper von 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, …”
Volltext

Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach von 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 …”
Volltext

Bayesian Conavigation: Dynamic Designing of the Material Digital Twins via Active Learning von Boris N. Slautin, Yongtao Liu, Hiroshi Funakubo, Rama K. Vasudevan, Maxim Ziatdinov, Sergei V. Kalinin

Volltext

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

Volltext

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

Volltext

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

Volltext

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

Volltext

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

Volltext

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

Volltext

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

Volltext

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

Volltext

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

Volltext