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Topic · Artificial Intelligence

Machine Learning and Algorithms

Machine Learning and Algorithms is a research topic within Artificial Intelligence. Science Explorer counts 18k research works in it since 1954. 24.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers revolves around the topic of active learning in machine learning research. It covers various aspects such as semi-supervised learning, deep learning, Gaussian processes, image classification, text categorization, batch mode active learning, statistical guarantees, and human-in-the-loop approaches.

  • Active Learning
  • Machine Learning
  • Semi-supervised Learning
  • Deep Learning
  • Gaussian Processes
  • Image Classification
  • Text Categorization
  • Batch Mode
  • Statistical Guarantees
  • Human-in-the-loop
Research works
18k
fractional, since 1954
In the world top 10%
4.3k
per year above
Top-10% rate
24.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+17%
the tick is no change

Which countries lead Machine Learning and Algorithms research?

By volume, the United States and China publish the most (715 and 522 works in 2022–2025).

By volume, 2022–2025

  1. 1 United States 715 works
  2. 2 China 522 works
  3. 3 Germany 198 works
  4. 4 United Kingdom 147 works
  5. 5 France 120 works
  6. 6 India 115 works
  7. 7 Japan 98 works
  8. 8 Canada 84 works
  9. 9 Italy 83 works
  10. 10 Australia 60 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

United States: 33.4%China: 24.3%Germany: 9.2%United Kingdom: 6.9%6 others listed: 26.2%33%largest
United States715 · 33.4%China522 · 24.3%Germany198 · 9.2%United Kingdom147 · 6.9%6 others listed561 · 26.2%

Shares of the rows listed above, not of the whole node.

Which institutions lead Machine Learning and Algorithms research?

By volume in 2022–2025, Massachusetts Institute of Technology publishes the most Machine Learning and Algorithms research, followed by Stanford University and University of California, Berkeley.

By volume, 2022–2025

  1. 1 Massachusetts Institute of TechnologyUnited States 20 works
  2. 2 Stanford UniversityUnited States 19 works
  3. 3 University of California, BerkeleyUnited States 19 works
  4. 4 Carnegie Mellon UniversityUnited States 18 works
  5. 5 Tsinghua UniversityChina 18 works
  6. 6 National University of SingaporeSingapore 17 works
  7. 7 Centre National de la Recherche ScientifiqueFrance 17 works
  8. 8 Nanjing UniversityChina 15 works
  9. 9 Georgia Institute of TechnologyUnited States 15 works
  10. 10 Technion – Israel Institute of TechnologyIsrael 14 works

Who are the leading researchers in Machine Learning and Algorithms?

The most-cited researchers publishing on Machine Learning and Algorithms include Li Fei-Fei, Yoshua Bengio and Sebastian Thrun.

  1. 1 Li Fei-Fei United States 17k citations
  2. 2 Yoshua Bengio Canada 17k citations
  3. 3 Sebastian Thrun United States 10k citations
  4. 4 Ion Stoica United States 9.6k citations
  5. 5 Quoc V. Le United States 8.6k citations
  6. 6 Ronald L. Rivest United States 7.3k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Machine Learning and Algorithms research done?

The largest centres of Machine Learning and Algorithms research in 2022–2025 are Beijing (China), Shanghai (China), Tokyo (Japan) and Nanjing (China).

Largest cities, 2022–2025

  1. 1 Beijing China 127 works
  2. 2 Shanghai China 50 works
  3. 3 Tokyo Japan 44 works
  4. 4 Nanjing China 41 works
  5. 5 Paris France 40 works
  6. 6 London United Kingdom 37 works
  7. 7 Singapore Singapore 35 works
  8. 8 New York United States 31 works
  9. 9 Seoul South Korea 30 works
  10. 10 Cambridge United States 30 works
See Machine Learning and Algorithms on the map

Where is the best place to study Machine Learning and Algorithms?

Among universities, judged by research, Massachusetts Institute of Technology, Chinese University of Hong Kong and Carnegie Mellon University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 20.34%fractional works in this node (log) →share in the world top 10% →Massachusetts Institute of Technology: 20, 16.4%Chinese University of Hong Kong: 9, 33.8%Carnegie Mellon University: 18, 12.9%University of California, Berkeley: 19, 21.5%National University of Singapore: 17, 14.7%Radboud University Nijmegen: 8, 30.8%Stanford University: 19, 17.4%ETH Zurich: 13, 14.1%Tsinghua University: 18, 28.1%Nanjing University: 15, 13.7%Chinese University o…University of Califo…Massachusetts Instit…Carnegie Mellon Univ…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Massachusetts Institute of TechnologyUnited States 64.316.4%11.5×20 +4.7%
2 Chinese University of Hong KongHong Kong 60.533.8%4.8×9 +69.7%
3 Carnegie Mellon UniversityUnited States 59.812.9%16.2×18 +41.0%
4 University of California, BerkeleyUnited States 59.221.5%8.0×19 -19.3%
5 National University of SingaporeSingapore 55.614.7%6.4×17 +11.6%
6 Radboud University NijmegenNetherlands 53.030.8%7.1×8 -2.2%
7 Stanford UniversityUnited States 52.417.4%5.7×19 +28.9%
8 ETH ZurichSwitzerland 51.014.1%7.0×13 +49.0%
9 Tsinghua UniversityChina 50.028.1%3.2×18 -15.8%
10 Nanjing UniversityChina 49.613.7%7.1×15 +85.0%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Machine Learning and Algorithms research growing?

Output in 2018–2022 was 17% higher than in 2013–2017, peaking in 2025. The fastest-growing topics are Machine Learning and Algorithms.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.