Search Results - Computer Science - Machine Learning

Computer science: The learning machines by Jones, Nicola

“…Using massive amounts of data to recognize photos and speech, deep-learning computers are taking a big step towards true artificial intelligence…”
Get full text

Out-of-Distributional Risk Bounds for Neural Operators with Applications to Thehelmholtz Equation by Lara Benitez, Jose Antonio, Furuya, Takashi, Faucher, Florian, Kratsios, Anastasis, Tricoche, Xavier, de Hoop, Maarten

Get full text

Learning and controlling the source-filter representation of speech with a variational autoencoder by Sadok, Samir, Leglaive, Simon, Girin, Laurent, Alameda-Pineda, Xavier, Seguier, Renaud

Get full text

STEM Workshops and Students’ Interest in Mathematics, Physics, and Computer Science: Machine Learning Approach by Milenković, Aleksandar, Ostojić, Dragutin, Rajković, Dalibor, Milikić, Milan

“… This study aims to examine the attitudes of students aged 14-17 regarding whether their participation in STEM workshops integrating content from mathematics, physics, and computer science contributes…”
Get full text

Fe-based superconducting transition temperature modeling by machine learning: A computer science method by Hu, Zhiyuan

“…s. To avoid the difficulties in measuring transition temperature, in this paper, we adopt a machine learning…”
Get full text

Machine Learning in High Energy Physics Community White Paper by 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, followed by an explosion of applications…”
Get full text

Teaching Quantum Machine Learning in Computer Science by De Luca, Gennaro, Chen, Yinong

“… The application of quantum computing to machine learning (i.e., quantum machine learning) is similarly growing rapidly…”
Get full text

Corrigendum to “Dealing with high dimensional multi-view data: A comprehensive review of non-negative matrix factorization approaches in data mining and machine learning” [Computer Science Review 58 (2025) 100788] by Soleymani, Nafiseh, Moattar, Mohammad Hossein, Sheibani, Reza

Get full text

Machine Learning in Non-Stationary Environments: Introduction to Covariate Shift Adaptation by Sugiyama, Masashi, Kawanabe, Motoaki

“…As the power of computing has grown over the past few decades, the field of machine learning has advanced rapidly in both theory and practice…”
Get full text

Examining the Impact of Mathematics Ancillary Courses on Computational Programming Intelligence of Computer Science Students Using Machine Learning Techniques by Ukekwe, Emmanuel Chukwudi, Ezeora, Nnamdi Johnson, Obayi, Adaora Angela, Asogwa, Caroline Ngozi, Ezugwu, Assumpta Obianuju, Adegoke, Folakemi O., Raiyetumbi, Jude, Tenuche, Bashir

“…Mathematics courses make up a good percentage of the undergraduate curriculum in the Computer Science and Engineering discipline…”
Get full text

Section focused on machine learning methods for high-level cognitive capabilities in robotics by Inamura, Tetsunari, Yokoyama, Hiroki, Ugur, Emre, Hinaut, Xavier, Beetze, Michael, Taniguchi, Tadahiro

Get full text

Guest Editorial Special Issue on Continual Unsupervised Sensorimotor Learning by Navarro-Guerrero, Nicolas, Nguyen, Sao Mai, Oztop, Erhan, Zhong, Junpei

“…The pursuit of higher levels of autonomy and versatility in robotics is arguably led by two main factors. First, as we push robots out of the labs and…”
Get full text

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning by Avanzo, Michele, Stancanello, Joseph, Pirrone, Giovanni, Drigo, Annalisa, Retico, Alessandra

“…Artificial intelligence (AI), the wide spectrum of technologies aiming to give machines or computers the ability to perform human-like cognitive functions, began in the 1940s with the first abstract models of intelligent machines…”
Get full text

Dynamic Routing for Integrated Satellite-Terrestrial Networks: A Constrained Multi-Agent Reinforcement Learning Approach by 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…”
Get full text

SmartCS: Enabling the Creation of Machine Learning-Powered Computer Vision Mobile Apps for Citizen Science Applications without Coding by Khan, Fahim Hasan, De Silva, Akila, Dusek, Gregory, Davis, James, Pang, Alex

“… There are now software tools that facilitate the development of citizen science apps. However, apps developed with these tools rely on individual human skills to correctly collect useful data. Machine learning (ML…”
Get full text

Machine learning in glaucoma: a bibliometric analysis comparing computer science and medical fields' research by AlRyalat, Saif Aldeen, Al-Ryalat, Nosaiba, Ryalat, Soukaina

“…The aim of this study was to analyze the current literature on the use of machine learning in glaucoma, comparing the characteristics and citations received by articles on computer science focus or medical focus…”
Get full text

Parameterized Complexity in Machine Learning by Ganian, Robert

“… are “intractable” has been a core topic of theoretical computer science already since its inception. For the latter class, the parameterized complexity paradigm pioneered by Downey…”
Get full text

Editorial: Modeling Play in Early Infant Development by Shaw, Patricia, Lee, Mark, Shen, Qiang, Hirsh-Pasek, Kathy, Adolph, Karen E., Oudeyer, Pierre-Yves, Popp, Jill

“… [...]the new field of developmental robotics looks toward infant development for inspiration, data, and guidance, in order to build models of learning that may be useful both for better understanding…”
Get full text

Combining Deep Learning with Good Old-Fashioned Machine Learning by Sipper, Moshe

“…We present a comprehensive, stacking-based framework for combining deep learning with good old-fashioned machine learning, called Deep GOld…”
Get full text

A data science and machine learning approach to continuous analysis of Shakespeare's plays by Swisher, Charles, Shamir, Lior

“… Here we apply comprehensive machine learning analysis to the work of William Shakespeare…”
Get full text