VLDB 2026 Research / reviewers in the wild / expert
Sung-Kwong Park
dblp:21/6263
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1993
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Deep learning architectures and training · 25% Efficient and distributed learning · 25% Graph learning · 25% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning › model compression › quantization › quantized neural network
binary neural network |
0.0 | 1 | 1993 | Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field · IEEE Trans. Computers 1993 |
Machine learning › Optimization for machine learning
convergence guarantees |
0.0 | 1 | 1993 | Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field · IEEE Trans. Computers 1993 |
Machine learning › Graph learning
geometric learning |
0.0 | 1 | 1993 | Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field · IEEE Trans. Computers 1993 |
Machine learning › Deep learning architectures and training › feedforward neural network
multilayer neural network |
0.0 | 1 | 1993 | Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary Field · IEEE Trans. Computers 1993 |
Methods — techniques the papers use, named apart from their topics
unipolar binary neurons · 0.0geometrical expansion learning · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1993 | Geometrical Learning Algorithm for Multilayer Neural Networks in a Binary FieldabstractA geometrical expansion learning algorithm for multilayer neural networks using unipolar binary neurons with integer connection weights, which guarantees convergence for any Boolean function, is introduced. Neurons in the hidden layer develop as necessary without supervision. In addition, the computational amount is much less than that of the backpropagation algorithm.> Sung-Kwong Park |
IEEE Trans. Computers | 1 |