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Topic · Artificial Intelligence

Machine Learning and ELM

Machine Learning and ELM is a research topic within Artificial Intelligence. Science Explorer counts 14k research works in it since 1965. 9.9% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the theory, applications, and advancements in Extreme Learning Machines (ELM), a machine learning framework based on feedforward neural networks with random hidden nodes. The papers cover topics such as incremental learning, classification, regression, ensemble methods, kernel-based models, and their applications in various domains.

  • Extreme Learning Machine
  • Feedforward Networks
  • Incremental Learning
  • Random Hidden Nodes
  • Classification
  • Regression
  • Neural Networks
  • Online Sequential Learning
  • Ensemble Learning
  • Kernel Methods
Research works
14k
fractional, since 1965
In the world top 10%
1.4k
per year above
Top-10% rate
9.9%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+50%
the tick is no change
Largest producer
China
63% of the 10 largest, 2022-now
Machine Learning and ELM

Which countries lead Machine Learning and ELM research?

By volume, China and India publish the most (3.1k and 734 works in 2022-now).

By volume, 2022-now

  1. 1 China 3.1k works
  2. 2 India 734 works
  3. 3 United States 401 works
  4. 4 South Korea 143 works
  5. 5 Japan 111 works
  6. 6 United Kingdom 101 works
  7. 7 ?? 78 works
  8. 8 Indonesia 77 works
  9. 9 Türkiye 74 works
  10. 10 Germany 73 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.

China: 63.0%India: 15.1%United States: 8.3%South Korea: 3.0%6 others listed: 10.6%63%largest
China3,053 · 63.0%India734 · 15.1%United States401 · 8.3%South Korea143 · 3.0%6 others listed515 · 10.6%

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

Which institutions lead Machine Learning and ELM research?

By volume in 2022-now, Chinese Academy of Sciences publishes the most Machine Learning and ELM research, followed by South China University of Technology and Northeastern University.

By volume, 2022-now

  1. 1 Chinese Academy of SciencesChina 74 works
  2. 2 South China University of TechnologyChina 42 works
  3. 3 Northeastern UniversityChina 40 works
  4. 4 Beijing University of TechnologyChina 37 works
  5. 5 Northwestern Polytechnical UniversityChina 37 works
  6. 6 Zhejiang UniversityChina 37 works
  7. 7 Central South UniversityChina 36 works
  8. 8 University of Electronic Science and Technology of ChinaChina 35 works
  9. 9 Xi'an Jiaotong UniversityChina 35 works
  10. 10 Xidian UniversityChina 35 works

Who are the leading researchers in Machine Learning and ELM?

The most-cited researchers publishing on Machine Learning and ELM include Yoshua Bengio, Geoffrey E. Hinton and Xiaogang Wang.

  1. 1 Yoshua Bengio Canada 476k citations
  2. 2 Geoffrey E. Hinton Canada 454k citations
  3. 3 Xiaogang Wang China 260k citations
  4. 4 Yann LeCun United States 256k citations
  5. 5 Andrew Zisserman United Kingdom 255k citations
  6. 6 Vladimir N. Vapnik United States 227k citations
  7. 7 Kai You Wang China 224k citations
  8. 8 Jürgen Schmidhuber Switzerland 188k citations
  9. 9 Michael I. Jordan United States 182k citations

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

What are the most cited papers in Machine Learning and ELM?

The most cited paper in Machine Learning and ELM is "Adam: A Method for Stochastic Optimization" (Diederik P. Kingma, Jimmy Lei Ba, 2014), with 84,625 citations. Next come "Adam: A Method for Stochastic Optimization" (50,327) and "Support-vector networks" (41,211).

Most cited papers
#PaperReleasedCitations
1 Adam: A Method for Stochastic Optimization Diederik P. Kingma, Jimmy Lei Ba · arXiv (Cornell University) PreprintOpen access 22 Dec 2014 84,625
2 Adam: A Method for Stochastic Optimization Mandi, Jayanta, Canoy, Rocsildes, Bucarey, Víctor et al. · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) Open access May 2015 50,327
3 Support-vector networks Corinna Cortes, Vladimir N. Vapnik · Machine Learning Open access Sep 1995 41,211
4 Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe, Christian Szegedy · arXiv (Cornell University) PreprintOpen access 11 Feb 2015 24,392
5 Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories Chen, Tianyu, Wang, Zeyu, Li, Lin et al. · Leibniz international proceedings in informatics Conference paperOpen access 2025 16,321
All 100 most cited papers, with filters and sorting

Where is Machine Learning and ELM research done?

The largest centres of Machine Learning and ELM research in 2022-now are Beijing (China), Xi'an (China), Nanjing (China) and Shanghai (China).

Largest cities, 2022-now

  1. 1 BeijingChina 504 works
  2. 2 Xi'anChina 180 works
  3. 3 NanjingChina 173 works
  4. 4 ShanghaiChina 163 works
  5. 5 GuangzhouChina 142 works
  6. 6 WuhanChina 107 works
  7. 7 ChennaiIndia 101 works
  8. 8 ChengduChina 97 works
  9. 9 HangzhouChina 97 works
  10. 10 ChangshaChina 93 works
See Machine Learning and ELM on the map

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

Among universities, judged by research, Northwestern Polytechnical University, Xi'an Jiaotong University and Nanyang Technological 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%10%20%30%mean 16.65%fractional works in this field (log) →share of work in the world top 10% →Northwestern Polytec…Xi'an Jiaotong Unive…Nanyang Technologica…South China Universi…
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Northwestern Polytechnical UniversityChina 78.820.5%6.4×37 +25.2%
2Xi'an Jiaotong UniversityChina 71.819.2%3.9×35 +63.1%
3Nanyang Technological UniversitySingapore 70.915.8%5.9×24 -16.6%
4South China University of TechnologyChina 70.011.6%7.5×42 +76.5%
5Indian Institute of Technology IndoreIndia 68.827.4%13.3×12 +105.8%
6Zhejiang UniversityChina 68.315.5%2.7×37 +49.2%
7Southeast UniversityChina 67.615.5%3.6×27 +60.0%
8Central South UniversityChina 67.415.3%3.6×36 +44.7%
9Harbin Institute of TechnologyChina 67.314.3%4.1×34 +87.3%
10Xidian UniversityChina 65.811.4%9.4×35 +70.6%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Machine Learning and ELM, country by country

Is Machine Learning and ELM research growing?

Output in 2022-2025 was 50% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Machine Learning and ELM.

still counting19801990200020102020
grewheldshrank

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

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Machine Learning and ELM as a percentage of every work published that year, 0.025% in 2024.

still counting0.010%0.020%0.030%Machine Learning and ELM0.025% in 202519801990200020102020

Fractional works of Machine Learning and ELM over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Machine Learning and ELM9.9% in 2025200020102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

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.

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