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Topic · Artificial Intelligence

Stochastic Gradient Optimization Techniques

Stochastic Gradient Optimization Techniques is a research topic within Artificial Intelligence. Science Explorer counts 7.9k research works in it since 1965. 24.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the application of optimization methods in machine learning, particularly in the context of stochastic gradient descent, random projections, deep learning, convex optimization, matrix decompositions, and large-scale optimization. The papers explore various algorithms and techniques for improving the efficiency and effectiveness of machine learning models, with a specific emphasis on neural networks and generalization.

  • Stochastic Gradient Descent
  • Random Projections
  • Deep Learning
  • Convex Optimization
  • Matrix Decompositions
  • Approximation Algorithms
  • Large-Scale Optimization
  • Neural Networks
  • Coordinate Descent
  • Generalization
Research works
7.9k
fractional, since 1965
In the world top 10%
1.9k
per year above
Top-10% rate
24.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+182%
the tick is no change

Which countries lead Stochastic Gradient Optimization Techniques research?

By volume, China and the United States publish the most (1.1k and 720 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.1k works
  2. 2 United States 720 works
  3. 3 India 124 works
  4. 4 Germany 96 works
  5. 5 France 91 works
  6. 6 United Kingdom 87 works
  7. 7 South Korea 83 works
  8. 8 Canada 79 works
  9. 9 Japan 77 works
  10. 10 Hong Kong 77 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: 43.6%United States: 28.3%India: 4.9%Germany: 3.8%6 others listed: 19.4%44%largest
China1,110 · 43.6%United States720 · 28.3%India124 · 4.9%Germany96 · 3.8%6 others listed494 · 19.4%

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

Which institutions lead Stochastic Gradient Optimization Techniques research?

By volume in 2022–2025, Shanghai Jiao Tong University publishes the most Stochastic Gradient Optimization Techniques research, followed by Sun Yat-sen University and Tsinghua University.

By volume, 2022–2025

  1. 1 Shanghai Jiao Tong UniversityChina 28 works
  2. 2 Sun Yat-sen UniversityChina 28 works
  3. 3 Tsinghua UniversityChina 27 works
  4. 4 Hong Kong University of Science and TechnologyHong Kong 25 works
  5. 5 Zhejiang UniversityChina 23 works
  6. 6 University of Science and Technology of ChinaChina 23 works
  7. 7 Beijing University of Posts and TelecommunicationsChina 22 works
  8. 8 National University of Defense TechnologyChina 21 works
  9. 9 Peking UniversityChina 21 works
  10. 10 University of Electronic Science and Technology of ChinaChina 19 works

Who are the leading researchers in Stochastic Gradient Optimization Techniques?

The most-cited researchers publishing on Stochastic Gradient Optimization Techniques include Yoshua Bengio, Wei Liu and H. Vincent Poor.

  1. 1 Yoshua Bengio Canada 17k citations
  2. 2 Wei Liu China 9.7k citations
  3. 3 H. Vincent Poor United States 9.5k citations
  4. 4 Quoc V. Le United States 8.6k citations
  5. 5 Philip S. Yu United States 6.6k citations
  6. 6 Dacheng Tao Australia 6.1k citations
  7. 7 Michael I. Jordan United States 4.8k citations
  8. 8 Zhu Han United States 4.7k citations

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

Where is Stochastic Gradient Optimization Techniques research done?

The largest centres of Stochastic Gradient Optimization Techniques research in 2022–2025 are Beijing (China), Shanghai (China), Nanjing (China) and Guangzhou (China). Among places with at least 20 works in it, it is an unusually large share of all research in Hong Kong.

Largest cities, 2022–2025

  1. 1 Beijing China 233 works
  2. 2 Shanghai China 103 works
  3. 3 Nanjing China 73 works
  4. 4 Guangzhou China 66 works
  5. 5 Hong Kong China 60 works
  6. 6 Shenzhen China 57 works
  7. 7 Xi'an China 51 works
  8. 8 Seoul South Korea 50 works
  9. 9 Hangzhou China 49 works
  10. 10 Chongqing China 44 works

Where it is the local speciality

  1. Hong KongCN · 60.4 works6.0×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Stochastic Gradient Optimization Techniques than the world average.

See Stochastic Gradient Optimization Techniques on the map

Where is the best place to study Stochastic Gradient Optimization Techniques?

Among universities, judged by research, Hong Kong University of Science and Technology, Nanyang Technological University and École Polytechnique Fédérale de Lausanne 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 26.43%fractional works in this node (log) →share in the world top 10% →Hong Kong University of Science and Technology: 25, 33.7%Nanyang Technological University: 16, 45.2%École Polytechnique Fédérale de Lausanne: 15, 27.7%Beijing University of Posts and Telecommunications: 22, 28.0%Tsinghua University: 27, 30.6%KTH Royal Institute of Technology: 13, 19.0%Georgia Institute of Technology: 18, 22.4%Carnegie Mellon University: 16, 13.9%National University of Defense Technology: 21, 16.1%Sun Yat-sen University: 28, 27.7%Nanyang Technologica…Hong Kong University…Beijing University o…École Polytechnique …
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 Hong Kong University of Science and TechnologyHong Kong 82.433.7%20.6×25 +116.1%
2 Nanyang Technological UniversitySingapore 73.845.2%6.7×16 +103.8%
3 École Polytechnique Fédérale de LausanneSwitzerland 71.727.7%12.1×15 +188.5%
4 Beijing University of Posts and TelecommunicationsChina 64.128.0%11.8×22 —
5 Tsinghua UniversityChina 63.030.6%4.5×27 +477.3%
6 KTH Royal Institute of TechnologySweden 61.019.0%11.6×13 +169.0%
7 Georgia Institute of TechnologyUnited States 60.022.4%9.6×18 +101.9%
8 Carnegie Mellon UniversityUnited States 58.513.9%12.9×16 +273.4%
9 National University of Defense TechnologyChina 57.616.1%8.7×21 +416.7%
10 Sun Yat-sen UniversityChina 56.227.7%5.9×28 —

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 Stochastic Gradient Optimization Techniques research growing?

Output in 2018–2022 was 182% higher than in 2013–2017, peaking in 2026. The fastest-growing topics are Stochastic Gradient Optimization Techniques.

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.