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 China 1.1k works
- 2 United States 720 works
- 3 India 124 works
- 4 Germany 96 works
- 5 France 91 works
- 6 United Kingdom 87 works
- 7 South Korea 83 works
- 8 Canada 79 works
- 9 Japan 77 works
- 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.
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 Shanghai Jiao Tong University China 28 works
- 2 Sun Yat-sen University China 28 works
- 3 Tsinghua University China 27 works
- 4 Hong Kong University of Science and Technology Hong Kong 25 works
- 5 Zhejiang University China 23 works
- 6 University of Science and Technology of China China 23 works
- 7 Beijing University of Posts and Telecommunications China 22 works
- 8 National University of Defense Technology China 21 works
- 9 Peking University China 21 works
- 10 University of Electronic Science and Technology of China China 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 Yoshua Bengio 17k citations
- 2 Wei Liu 9.7k citations
- 3 H. Vincent Poor 9.5k citations
- 4 Quoc V. Le 8.6k citations
- 5 Philip S. Yu 6.6k citations
- 6 Dacheng Tao 6.1k citations
- 7 Michael I. Jordan 4.8k citations
- 8 Zhu Han 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
Where it is the local speciality
- Hong KongCN · 60.4 works6.0×
Location quotient: how much more of its research is in Stochastic Gradient Optimization Techniques than the world average.
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.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Hong Kong University of Science and Technology Hong Kong | 82.4 | 33.7% | 20.6× | 25 | +116.1% |
| 2 | Nanyang Technological University Singapore | 73.8 | 45.2% | 6.7× | 16 | +103.8% |
| 3 | École Polytechnique Fédérale de Lausanne Switzerland | 71.7 | 27.7% | 12.1× | 15 | +188.5% |
| 4 | Beijing University of Posts and Telecommunications China | 64.1 | 28.0% | 11.8× | 22 | — |
| 5 | Tsinghua University China | 63.0 | 30.6% | 4.5× | 27 | +477.3% |
| 6 | KTH Royal Institute of Technology Sweden | 61.0 | 19.0% | 11.6× | 13 | +169.0% |
| 7 | Georgia Institute of Technology United States | 60.0 | 22.4% | 9.6× | 18 | +101.9% |
| 8 | Carnegie Mellon University United States | 58.5 | 13.9% | 12.9× | 16 | +273.4% |
| 9 | National University of Defense Technology China | 57.6 | 16.1% | 8.7× | 21 | +416.7% |
| 10 | Sun Yat-sen University China | 56.2 | 27.7% | 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.
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.