Neural Networks and Applications
Neural Networks and Applications is a research topic within Artificial Intelligence. Science Explorer counts 99k research works in it since 1950. 15.1% of them reached the world's top 10% most cited for their field and year.
This cluster of papers covers a wide range of topics related to neural networks, including backpropagation learning, self-organizing maps, radial basis function networks, deep learning, and applications such as pattern classification and function approximation.
- Neural Networks
- Self-Organizing Maps
- Backpropagation Learning
- Radial Basis Function Networks
- Deep Learning
- Artificial Neural Networks
- Recurrent Neural Networks
- Feedforward Neural Networks
- Pattern Classification
- Function Approximation
- Research works
- 99k fractional, since 1950
- In the world top 10%
- 15k per year above
- Top-10% rate
- 15.1% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- +19% the tick is no change
Which countries lead Neural Networks and Applications research?
By volume, China and the United States publish the most (4.8k and 2.6k works in 2022–2025).
By volume, 2022–2025
- 1 China 4.8k works
- 2 United States 2.6k works
- 3 India 1.6k works
- 4 Japan 602 works
- 5 Germany 574 works
- 6 United Kingdom 526 works
- 7 France 492 works
- 8 Russia 452 works
- 9 Italy 441 works
- 10 South Korea 370 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 Neural Networks and Applications research?
By volume in 2022–2025, Tsinghua University publishes the most Neural Networks and Applications research, followed by University of Electronic Science and Technology of China and Shanghai Jiao Tong University.
By volume, 2022–2025
- 1 Tsinghua UniversityChina 74 works
- 2 University of Electronic Science and Technology of ChinaChina 73 works
- 3 Shanghai Jiao Tong UniversityChina 71 works
- 4 Southeast UniversityChina 69 works
- 5 École Polytechnique Fédérale de LausanneSwitzerland 66 works
- 6 Zhejiang UniversityChina 62 works
- 7 Xidian UniversityChina 60 works
- 8 Harbin Institute of TechnologyChina 60 works
- 9 Beihang UniversityChina 59 works
- 10 Chinese Academy of SciencesChina 57 works
Who are the leading researchers in Neural Networks and Applications?
The most-cited researchers publishing on Neural Networks and Applications include Geoffrey E. Hinton, Dumitru Erhan and Yoshua Bengio.
- 1 Geoffrey E. Hinton Canada 22k citations
- 2 Dumitru Erhan United States 17k citations
- 3 Yoshua Bengio Canada 17k citations
- 4 Robert Tibshirani United States 13k citations
- 5 Jerome H. Friedman United States 11k citations
- 6 Michael Maire United States 10k citations
- 7 Sebastian Thrun United States 10k citations
- 8 Wei Liu China 9.7k citations
- 9 Chih‐Jen Lin Taiwan 9.6k citations
- 10 H. Vincent Poor United States 9.5k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Neural Networks and Applications research done?
The largest centres of Neural Networks and Applications research in 2022–2025 are Beijing (China), Shanghai (China), Nanjing (China) and Xi'an (China). Among places with at least 20 works in it, it is an unusually large share of all research in Tijuana and El Paso.
Largest cities, 2022–2025
Where it is the local speciality
- TijuanaMX · 20.2 works19×
- El PasoUS · 24.1 works9.0×
Location quotient: how much more of its research is in Neural Networks and Applications than the world average.
Where is the best place to study Neural Networks and Applications?
Among universities, judged by research, École Polytechnique Fédérale de Lausanne, Shenzhen University and University of Memphis 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 | École Polytechnique Fédérale de LausanneSwitzerland | 53.5 | 7.4% | 9.5× | 66 | -36.0% |
| 2 | Shenzhen UniversityChina | 49.7 | 28.1% | 2.7× | 33 | +250.4% |
| 3 | University of MemphisUnited States | 48.5 | 16.1% | 14.0× | 28 | +2.4% |
| 4 | Vellore Institute of Technology UniversityIndia | 47.3 | 16.1% | 3.1× | 52 | +251.1% |
| 5 | City University of Hong KongHong Kong | 46.9 | 25.2% | 3.7× | 36 | +7.7% |
| 6 | Instituto Tecnológico de TijuanaMexico | 46.9 | 18.0% | 68.3× | 19 | +18.2% |
| 7 | Xidian UniversityChina | 46.6 | 26.3% | 4.9× | 60 | +2.2% |
| 8 | Southwest UniversityChina | 46.0 | 20.4% | 4.1× | 38 | +145.9% |
| 9 | China University of Petroleum, East ChinaChina | 45.3 | 34.4% | 2.9× | 23 | +159.5% |
| 10 | King Abdullah University of Science and TechnologySaudi Arabia | 45.0 | 22.7% | 4.3× | 20 | +96.3% |
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 Neural Networks and Applications research growing?
Output in 2018–2022 was 19% higher than in 2013–2017, peaking in 2024.
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.