Demo Data from EDBT 2026 / reviewers in the wild / expert
Bingwen Wang
dblp:40/4013
· DBLP profile ↗
8ranked-venue papers
0as first author
2since 2021 · last 2026
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 6 · 2 since 2021Applied, interdisciplinary, general and emerging computing · 2
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 · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Deep learning architectures and training › activation function
activation function design |
0.0 | 1 | 2004 | A fast learning algorithm of neural network with tunable activation function · Sci. China Ser. F Inf. Sci. 2004 |
Machine learning › Deep learning architectures and training › activation function
trainable activation function |
0.0 | 1 | 2004 | A fast learning algorithm of neural network with tunable activation function · Sci. China Ser. F Inf. Sci. 2004 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Improving adversarial transferability via semantic-style joint expectation perturbations
Bingwen Wang, Kang Deng, Anjie Peng |
Pattern Recognit. | 2 |
| 2022 | Multi-features guidance network for partial-to-partial point cloud registration
Wen Kang, Bingwen Wang, Qianhao Ning |
Neural Comput. Appl. | 5 |
| 2016 | A Frequency-Hopping Subspace-Based Optimization Method for Reconstruction of 2-D Large Uniaxial Anisotropic Scatterers With TE IlluminationabstractDue to the nonlinear property of large uniaxial anisotropic scatterers, many iterative optimization methods have a high risk of being trapped in local minima. In this paper, a frequency-hopping subspace-based optimization method (SOM) is proposed to reconstruct the relative permittivity distribution of 2-D large uniaxial anisotropic scatterers with transverse electrical (TE) illumination. This hybrid method utilizes the results obtained at lower frequency to provide good initial guesses for higher frequency reconstruction, which reduces the occurrence of local minima for the inversion at the higher frequency. For lower frequency, it can only obtain coarse resolution image although it is unable to show the details of the scatterers. However, this coarse image provides a priori information for the reconstruction at higher frequencies to get finer resolution. Numerical examples demonstrate that the proposed hybrid method can effectively rebuild large uniaxial anisotropic scatterers (six wavelengths) with higher stability compared with conventional SOM that uses only single-frequency data. Yulang Liu, Zhiqin Zhao, Yaohui Yang, Bingwen Wang, Xiaozhang Zhu, Zaiping Nie, Qing Huo Liu |
IEEE Trans. Geosci. Remote. Sens. | 4 |
| 2009 | RTKPS: A Key Pre-distribution Scheme Based on Rooted-Tree in Wireless Sensor and Actor Network
Zhicheng Dai, Zhi Li 0027, Bingwen Wang, Qiang Tang 0004 |
ISNN (3) | 3 |
| 2009 | Back Propagation Neural Network Based Lifetime Analysis of Wireless Sensor Network
Bingwen Wang, Xiaoya Hu |
ISNN (3) | 2 |
| 2009 | Design and Implementation of the Structure Health Monitoring System for Bridge Based on Wireless Sensor Network
An Yin, Bingwen Wang, Xiaoya Hu |
ISNN (3) | 2 |
| 2004 | A fast learning algorithm of neural network with tunable activation function
Bingwen Wang |
Sci. China Ser. F Inf. Sci. | 2 |
| 2004 | A New Multi-output Neural Model with Tunable Activation Function and its Applications
Bingwen Wang, Fangxin Chen, Liang Cheng 0001 |
Neural Process. Lett. | 2 |