Machine learning (English Wikipedia)

Analysis of information sources in references of the Wikipedia article "Machine learning" in English language version.

Last modified: 05:38:25, 2 November 2026 (UTC)

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acm.org (Global: 1,200^th place; English: 821^st place)

dl.acm.org

Shapiro, Ehud Y. "The model inference system Archived 2023-04-06 at the Wayback Machine." Proceedings of the 7th international joint conference on Artificial intelligence-Volume 2. Morgan Kaufmann Publishers Inc., 1981.
Ji, Ziwei; Lee, Nayeon; Frieske, Rita; Yu, Tiezheng; Su, Dan; Xu, Yan; Ishii, Etsuko; Bang, Ye Jin; Madotto, Andrea; Fung, Pascale (3 March 2023). "Survey of Hallucination in Natural Language Generation". ACM Computing Surveys. 55 (12). New York, NY, USA: Association for Computing Machinery: 248:1–248:38. arXiv:2202.03629. doi:10.1145/3571730. ISSN 0360-0300.

adsabs.harvard.edu (Global: 14^th place; English: 9^th place)

ui.adsabs.harvard.edu

Milner, Peter M. (1993). "The Mind and Donald O. Hebb" (PDF). Scientific American. 268 (1): 124–129. Bibcode:1993SciAm.268a.124M. doi:10.1038/scientificamerican0193-124. ISSN 0036-8733. JSTOR 24941344. PMID 8418480.
LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015). "Deep learning". Nature. 521 (7553): 436–444. Bibcode:2015Natur.521..436L. doi:10.1038/nature14539. PMID 26017442.
Silver, David; et al. (2016). "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484–489. Bibcode:2016Natur.529..484S. doi:10.1038/nature16961. PMID 26819042.
Mashaghi, A.; Ramezanpour, A. (16 March 2018). "Statistical physics of medical diagnostics: Study of a probabilistic model". Physical Review E. 97 (3–1) 032118. arXiv:1803.10019. Bibcode:2018PhRvE..97c2118M. doi:10.1103/PhysRevE.97.032118. PMID 29776109. S2CID 4955393.
Jordan, M. I.; Mitchell, T. M. (17 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218.
Okolie, Jude A.; Savage, Shauna; Ogbaga, Chukwuma C.; Gunes, Burcu (June 2022). "Assessing the potential of machine learning methods to study the removal of pharmaceuticals from wastewater using biochar or activated carbon". Total Environment Research Themes. 1–2 100001. Bibcode:2022TERT....100001O. doi:10.1016/j.totert.2022.100001. S2CID 249022386.
Roweis, Sam T.; Saul, Lawrence K. (22 December 2000). "Nonlinear Dimensionality Reduction by Locally Linear Embedding". Science. 290 (5500): 2323–2326. Bibcode:2000Sci...290.2323R. doi:10.1126/science.290.5500.2323. PMID 11125150.
Y. Bengio; A. Courville; P. Vincent (2013). "Representation Learning: A Review and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798–1828. arXiv:1206.5538. Bibcode:2013ITPAM..35.1798B. doi:10.1109/tpami.2013.50. PMID 23787338. S2CID 393948.
Lu, Haiping; Plataniotis, K.N.; Venetsanopoulos, A.N. (2011). "A Survey of Multilinear Subspace Learning for Tensor Data" (PDF). Pattern Recognition. 44 (7): 1540–1551. Bibcode:2011PatRe..44.1540L. doi:10.1016/j.patcog.2011.01.004. Archived (PDF) from the original on 10 July 2019. Retrieved 4 September 2015.
Tillmann, A. M. (2015). "On the Computational Intractability of Exact and Approximate Dictionary Learning". IEEE Signal Processing Letters. 22 (1): 45–49. arXiv:1405.6664. Bibcode:2015ISPL...22...45T. doi:10.1109/LSP.2014.2345761. S2CID 13342762.
Bassel, George W.; Glaab, Enrico; Marquez, Julietta; Holdsworth, Michael J.; Bacardit, Jaume (1 September 2011). "Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets". The Plant Cell. 23 (9): 3101–3116. Bibcode:2011PlanC..23.3101B. doi:10.1105/tpc.111.088153. ISSN 1532-298X. PMC 3203449. PMID 21896882.
Michie, D.; Spiegelhalter, D. J.; Taylor, C. C. (1994). Machine Learning, Neural and Statistical Classification. Bibcode:1994mlns.book.....M. ISBN 978-0-13-106360-0. {{cite book}}: |journal= ignored (help)
Zhang, Jun; Zhan, Zhi-hui; Lin, Ying; Chen, Ni; Gong, Yue-jiao; Zhong, Jing-hui; Chung, Henry S.H.; Li, Yun; Shi, Yu-hui (2011). "Evolutionary Computation Meets Machine Learning: A Survey". IEEE Computational Intelligence Magazine. 6 (4): 68–75. Bibcode:2011ICIM....6d..68Z. doi:10.1109/mci.2011.942584. S2CID 6760276.
Yoosefzadeh-Najafabadi, Mohsen; Hugh, Earl; Tulpan, Dan; Sulik, John; Eskandari, Milad (2021). "Application of Machine Learning Algorithms in Plant Breeding: Predicting Yield From Hyperspectral Reflectance in Soybean?". Front. Plant Sci. 11 624273. Bibcode:2021FrPS...1124273Y. doi:10.3389/fpls.2020.624273. PMC 7835636. PMID 33510761.
Ivanenko, Mikhail; Smolik, Waldemar T.; Wanta, Damian; Midura, Mateusz; Wróblewski, Przemysław; Hou, Xiaohan; Yan, Xiaoheng (2023). "Image Reconstruction Using Supervised Learning in Wearable Electrical Impedance Tomography of the Thorax". Sensors. 23 (18): 7774. Bibcode:2023Senso..23.7774I. doi:10.3390/s23187774. PMC 10538128. PMID 37765831.
Sun, Yuran; Huang, Shih-Kai; Zhao, Xilei (1 February 2024). "Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods". International Journal of Disaster Risk Science. 15 (1): 134–148. arXiv:2303.06557. Bibcode:2024IJDRS..15..134S. doi:10.1007/s13753-024-00541-1. ISSN 2192-6395.
Wang, Ke; Shi, Xiupeng; Goh, Algena Pei Xuan; Qian, Shunzhi (June 2019). "A machine learning based study on pedestrian movement dynamics under emergency evacuation". Fire Safety Journal. 106: 163–176. Bibcode:2019FirSJ.106..163W. doi:10.1016/j.firesaf.2019.04.008. hdl:10356/143390.
Hu, Tongxi; Zhang, Xuesong; Bohrer, Gil; Liu, Yanlan; Zhou, Yuyu; Martin, Jay; LI, Yang; Zhao, Kaiguang (2023). "Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield". Agricultural and Forest Meteorology. 336 109458. Bibcode:2023AgFM..33609458H. doi:10.1016/j.agrformet.2023.109458. S2CID 258552400.
Caliskan, Aylin; Bryson, Joanna J.; Narayanan, Arvind (14 April 2017). "Semantics derived automatically from language corpora contain human-like biases". Science. 356 (6334): 183–186. arXiv:1608.07187. Bibcode:2017Sci...356..183C. doi:10.1126/science.aal4230. ISSN 0036-8075. PMID 28408601. S2CID 23163324.
AlSelek, Mohammad; Alcaraz-Calero, Jose M.; Wang, Qi (2024). "Dynamic AI-IoT: Enabling Updatable AI Models in Ultralow-Power 5G IoT Devices". IEEE Internet of Things Journal. 11 (8): 14192–14205. Bibcode:2024IITJ...1114192A. doi:10.1109/JIOT.2023.3340858.

airesearch.com (Global: low place; English: low place)

Research, AI (23 October 2015). "Deep Neural Networks for Acoustic Modeling in Speech Recognition". airesearch.com. Archived from the original on 1 February 2016. Retrieved 23 October 2015.

analyticsindiamag.com (Global: low place; English: low place)

"A Beginner's Guide To Machine learning For Embedded Systems". Analytics India Magazine. 2 June 2021. Archived from the original on 18 January 2022. Retrieved 17 January 2022.

apa.org (Global: 1,327^th place; English: 1,093^rd place)

psycnet.apa.org

McClelland, J. L., McNaughton, B. L., & O'Reilly, R. C. (1995). Why there are complementary learning systems in the hippocampus and neocortex: Insights from the successes and failures of connectionist models of learning and memory. Psychological Review, 102(3), 419–457.

archive.org (Global: 6^th place; English: 6^th place)

Mitchell, T. (1997). Machine Learning. McGraw Hill. pp. 2. ISBN 978-0-07-042807-2.
Sarle, Warren S. (1994). "Neural Networks and statistical models". SUGI 19: proceedings of the Nineteenth Annual SAS Users Group International Conference. SAS Institute. pp. 1538–50. ISBN 978-1-55544-611-6. OCLC 35546178.
Alpaydin, Ethem (2010). Introduction to Machine Learning. London: The MIT Press. ISBN 978-0-262-01243-0. Retrieved 4 February 2017.
Jordan, M. I.; Mitchell, T. M. (17 July 2015). "Machine learning: Trends, perspectives, and prospects". Science. 349 (6245): 255–260. Bibcode:2015Sci...349..255J. doi:10.1126/science.aaa8415. PMID 26185243. S2CID 677218.
Jordan, Michael I.; Bishop, Christopher M. (2004). "Neural Networks". In Allen B. Tucker (ed.). Computer Science Handbook, Second Edition (Section VII: Intelligent Systems). Boca Raton, Florida: Chapman & Hall/CRC Press LLC. ISBN 978-1-58488-360-9.
Michie, D.; Spiegelhalter, D. J.; Taylor, C. C. (1994). Machine Learning, Neural and Statistical Classification. Bibcode:1994mlns.book.....M. ISBN 978-0-13-106360-0. {{cite book}}: |journal= ignored (help)

arstechnica.com (Global: 473^rd place; English: 338^th place)

Edwards, Benj (28 September 2023). "AI language models can exceed PNG and FLAC in lossless compression, says study". Ars Technica. Archived from the original on 7 March 2024. Retrieved 7 March 2024.
Edwards, Benj (6 April 2023). "Why ChatGPT and Bing Chat are so good at making things up". Ars Technica. Archived from the original on 11 June 2023. Retrieved 11 June 2023.

arxiv.org (Global: 49^th place; English: 31^st place)

Mikolov, Tomas; Chen, Kai; Corrado, Greg; Dean, Jeffrey (2013). "Efficient Estimation of Word Representations in Vector Space". arXiv:1301.3781 [cs.CL].
Mentzer, Fabian; Toderici, George; Tschannen, Michael; Agustsson, Eirikur (2020). "High-Fidelity Generative Image Compression". arXiv:2006.09965 [eess.IV].
Delétang, Grégoire; Ruoss, Anian; Duquenne, Paul-Ambroise; Catt, Elliot; Genewein, Tim; Mattern, Christopher; Grau-Moya, Jordi; Li Kevin Wenliang; Aitchison, Matthew; Orseau, Laurent; Hutter, Marcus; Veness, Joel (2023). "Language Modeling is Compression". arXiv:2309.10668 [cs.LG].
Mashaghi, A.; Ramezanpour, A. (16 March 2018). "Statistical physics of medical diagnostics: Study of a probabilistic model". Physical Review E. 97 (3–1) 032118. arXiv:1803.10019. Bibcode:2018PhRvE..97c2118M. doi:10.1103/PhysRevE.97.032118. PMID 29776109. S2CID 4955393.
Misra, Ishan; Maaten, Laurens van der (2020). Self-Supervised Learning of Pretext-Invariant Representations. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, WA, US: IEEE. pp. 6707–6717. arXiv:1912.01991. doi:10.1109/CVPR42600.2020.00674.
Jaiswal, Ashish; Babu, Ashwin Ramesh; Zadeh, Mohammad Zaki; Banerjee, Debapriya; Makedon, Fillia (March 2021). "A Survey on Contrastive Self-Supervised Learning". Technologies. 9 (1): 2. arXiv:2011.00362. doi:10.3390/technologies9010002. ISSN 2227-7080.
Y. Bengio; A. Courville; P. Vincent (2013). "Representation Learning: A Review and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence. 35 (8): 1798–1828. arXiv:1206.5538. Bibcode:2013ITPAM..35.1798B. doi:10.1109/tpami.2013.50. PMID 23787338. S2CID 393948.
Tillmann, A. M. (2015). "On the Computational Intractability of Exact and Approximate Dictionary Learning". IEEE Signal Processing Letters. 22 (1): 45–49. arXiv:1405.6664. Bibcode:2015ISPL...22...45T. doi:10.1109/LSP.2014.2345761. S2CID 13342762.
Fleer, S.; Moringen, A.; Klatzky, R. L.; Ritter, H. (2020). "Learning efficient haptic shape exploration with a rigid tactile sensor array, S. Fleer, A. Moringen, R. Klatzky, H. Ritter". PLOS ONE. 15 (1) e0226880. arXiv:1902.07501. doi:10.1371/journal.pone.0226880. PMC 6940144. PMID 31896135.
Sun, Yuran; Huang, Shih-Kai; Zhao, Xilei (1 February 2024). "Predicting Hurricane Evacuation Decisions with Interpretable Machine Learning Methods". International Journal of Disaster Risk Science. 15 (1): 134–148. arXiv:2303.06557. Bibcode:2024IJDRS..15..134S. doi:10.1007/s13753-024-00541-1. ISSN 2192-6395.
Alemohammad, Sina; Casco-Rodriguez, Josue; Luzi, Lorenzo; Ahmed Imtiaz Humayun; Babaei, Hossein; LeJeune, Daniel; Siahkoohi, Ali; Baraniuk, Richard G. (2023). "Self-Consuming Generative Models Go MAD". arXiv:2307.01850 [cs.LG].
Liang, Kaiqu; Hu, Haimin; Zhao, Xuandong; Song, Dawn; Griffiths, Thomas L.; Fernández Fisac, Jaime (2025). "Machine Bullshit: Characterizing the Emergent Disregard for Truth in Large Language Models". arXiv:2507.07484 [cs.CL].
Ortega, Pedro A.; Kunesch, Markus; Delétang, Grégoire; Genewein, Tim; Grau-Moya, Jordi; Veness, Joel; Buchli, Jonas; Degrave, Jonas; Piot, Bilal; Perolat, Julien; Everitt, Tom; Tallec, Corentin; Parisotto, Emilio; Erez, Tom; Chen, Yutian; Reed, Scott; Hutter, Marcus; Nando de Freitas; Legg, Shane (2021). Shaking the foundations: Delusions in sequence models for interaction and control (Preprint). arXiv:2110.10819.
de Wynter, Adrian; Wang, Xun; Sokolov, Alex; Gu, Qilong; Chen, Si-Qing (September 2023). "An evaluation on large language model outputs: Discourse and memorization". Natural Language Processing Journal. 4 100024. arXiv:2304.08637. doi:10.1016/j.nlp.2023.100024.
Kang, Eunsuk; Shaw, Mary (2024). "tl;dr: Chill, y'all: AI Will Not Devour SE". Proceedings of the 2024 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software. pp. 303–315. arXiv:2409.00764. doi:10.1145/3689492.3689816. ISBN 979-8-4007-1215-9.
Ji, Ziwei; Lee, Nayeon; Frieske, Rita; Yu, Tiezheng; Su, Dan; Xu, Yan; Ishii, Etsuko; Bang, Ye Jin; Madotto, Andrea; Fung, Pascale (3 March 2023). "Survey of Hallucination in Natural Language Generation". ACM Computing Surveys. 55 (12). New York, NY, USA: Association for Computing Machinery: 248:1–248:38. arXiv:2202.03629. doi:10.1145/3571730. ISSN 0360-0300.
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; Vladu, A. (4 September 2019). "Towards deep learning models resistant to adversarial attacks". arXiv:1706.06083 [stat.ML].
Goldwasser, Shafi; Kim, Michael P.; Vaikuntanathan, Vinod; Zamir, Or (14 April 2022). "Planting Undetectable Backdoors in Machine Learning Models". arXiv:2204.06974 [cs.LG].
Caliskan, Aylin; Bryson, Joanna J.; Narayanan, Arvind (14 April 2017). "Semantics derived automatically from language corpora contain human-like biases". Science. 356 (6334): 183–186. arXiv:1608.07187. Bibcode:2017Sci...356..183C. doi:10.1126/science.aal4230. ISSN 0036-8075. PMID 28408601. S2CID 23163324.
M.O.R. Prates; P.H.C. Avelar; L.C. Lamb (11 March 2019). "Assessing Gender Bias in Machine Translation – A Case Study with Google Translate". arXiv:1809.02208 [cs.CY].
Jouppi, Norman P.; Young, Cliff; Patil, Nishant; Patterson, David; Agrawal, Gaurav; Bajwa, Raminder; Bates, Sarah; Bhatia, Suresh; Boden, Nan; Borchers, Al; Boyle, Rick; Cantin, Pierre-luc; Chao, Clifford; Clark, Chris; Coriell, Jeremy (24 June 2017). "In-Datacenter Performance Analysis of a Tensor Processing Unit". Proceedings of the 44th Annual International Symposium on Computer Architecture. ISCA '17. New York, NY, US: Association for Computing Machinery. pp. 1–12. arXiv:1704.04760. doi:10.1145/3079856.3080246. ISBN 978-1-4503-4892-8.
Giri, Davide; Chiu, Kuan-Lin; Di Guglielmo, Giuseppe; Mantovani, Paolo; Carloni, Luca P. (15 June 2020). "ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning". 2020 Design, Automation & Test in Europe Conference & Exhibition (DATE). pp. 1049–1054. arXiv:2004.03640. doi:10.23919/DATE48585.2020.9116317. ISBN 978-3-9819263-4-7. S2CID 210928161. Archived from the original on 18 January 2022. Retrieved 17 January 2022.

axios.com (Global: 1,139^th place; English: 623^rd place)

"AI could choke on its own exhaust as it fills the web". 28 August 2023.^{[full citation needed]}

bbc.com (Global: 26^th place; English: 23^rd place)

"Single pixel change fools AI programs". BBC News. 3 November 2017. Archived from the original on 22 March 2018. Retrieved 12 March 2018.

berkeley.edu (Global: 621^st place; English: 429^th place)

cltc.berkeley.edu

"Adversarial Machine Learning – CLTC UC Berkeley Center for Long-Term Cybersecurity". CLTC. Archived from the original on 17 May 2022. Retrieved 25 May 2022.

bloomberg.com (Global: 118^th place; English: 85^th place)

"Why Machine Learning Models Often Fail to Learn: QuickTake Q&A". Bloomberg.com. 10 November 2016. Archived from the original on 20 March 2017. Retrieved 10 April 2017.

books.google.com (Global: 3^rd place; English: 3^rd place)

Le Roux, Nicolas; Bengio, Yoshua; Fitzgibbon, Andrew (2012). "Improving First and Second-Order Methods by Modeling Uncertainty". In Sra, Suvrit; Nowozin, Sebastian; Wright, Stephen J. (eds.). Optimization for Machine Learning. MIT Press. p. 404. ISBN 978-0-262-01646-9. Archived from the original on 17 January 2023. Retrieved 12 November 2020.
Alpaydin, Ethem (2010). Introduction to Machine Learning. MIT Press. p. 9. ISBN 978-0-262-01243-0. Archived from the original on 17 January 2023. Retrieved 25 November 2018.
Yoshua Bengio (2009). Learning Deep Architectures for AI. Now Publishers Inc. pp. 1–3. ISBN 978-1-60198-294-0. Archived from the original on 17 January 2023. Retrieved 15 February 2016.
Zhang, C. and Zhang, S., 2002. Association rule mining: models and algorithms. Springer-Verlag.
De Castro, Leandro Nunes, and Jonathan Timmis. Artificial immune systems: a new computational intelligence approach. Springer Science & Business Media, 2002.

bris.ac.uk (Global: 5,759^th place; English: 4,110^th place)

research-information.bris.ac.uk

Fafoutis, Xenofon; Marchegiani, Letizia; Elsts, Atis; Pope, James; Piechocki, Robert; Craddock, Ian (7 May 2018). "Extending the battery lifetime of wearable sensors with embedded machine learning". 2018 IEEE 4th World Forum on Internet of Things (WF-IoT). pp. 269–274. doi:10.1109/WF-IoT.2018.8355116. hdl:1983/b8fdb58b-7114-45c6-82e4-4ab239c1327f. ISBN 978-1-4673-9944-9. S2CID 19192912. Archived from the original on 18 January 2022. Retrieved 17 January 2022.

businessweekly.co.uk (Global: low place; English: low place)

Quested, Tony. "Smartphones get smarter with Essex innovation". Business Weekly. Archived from the original on 24 June 2021. Retrieved 17 June 2021.

cambridge.org (Global: 209^th place; English: 151^st place)

Marcos M. López de Prado (2010). Machine Learning for Asset Managers. Cambridge University Press. ISBN 9781108883658

cell.com (Global: 2,821^st place; English: 4,262^nd place)

Ohmae S, Ohmae K. Brain-AI convergence: Generative world models and hierarchical attention for human intelligence Patterns, 2026; 7.
Whittington J, Bogacz R.Theories of Error Back-Propagation in the Brain Trends in Cognitive Sciences, 2019; 23, 235-250.
Kumaran D, Hassabis D, McClelland J What Learning Systems do Intelligent Agents Need? Complementary Learning Systems Theory Updated Trends in Cognitive Sciences, 20, 512-534.

cfainstitute.org (Global: low place; English: low place)

Machine learning is included in the CFA Curriculum; see: {{Webarchive|url=https://www.cfainstitute.org/

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