Wengang Zhang
Artificial Intelligence · 2013–2021
A researcher in the Science corpus: 12 papers; trusts 17 researchers and is trusted by 14 researchers here, as inferred from the citation graph.
Subfields
- Artificial Intelligence — 63 citations into it
- Civil and Structural Engineering — 35 citations into it; #984 of 6,160 by trust here, #975 by citations
- Management, Monitoring, Policy and Law — 27 citations into it; #1,331 of 2,821 by trust here, #1,014 by citations
- Safety, Risk, Reliability and Quality — 20 citations into it; #458 of 1,220 by trust here, #110 by citations
- Statistics and Probability — 18 citations into it
- Ocean Engineering — 16 citations into it
- Mechanics of Materials — 14 citations into it
- Model Architectures — 14 citations into it
Whom Wengang Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Leo Breiman
- A. Goh
- Jerome H. Friedman
- David E. Rumelhart
- Yoshua Bengio
- Geoffrey E. Hinton
- David Ruppert
- Robert Tibshirani
- Trevor J. Hastie
- Klaus Nordhausen
Who trusts Wengang Zhang overall
- Chongzhi Wu
- A. Goh
- Runhong Zhang
- Yingui Qiu
- Yongqin Li
- Jian Zhou
- Saif Alzabeebee
- Han-long Liu
- Yazhou Xie
- Hongrui Li
Papers from Wengang Zhang's perspective
Ordered by how strongly the researchers Wengang Zhang trusts endorse each paper. Same corpus, a different order for every researcher.
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- ImageNet classification with deep convolutional neural networks
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Learning Multiple Layers of Features from Tiny Images
- Going deeper with convolutions
Their papers
- Prediction of undrained shear strength using extreme gradient boosting and random forest based on Bayesian optimization (2021) — 770 citations
- Application of deep learning algorithms in geotechnical engineering: a short critical review (2021) — 472 citations
- Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization (2021) — 358 citations
- Predicting TBM penetration rate in hard rock condition: A comparative study among six XGB-based metaheuristic techniques (2020) — 245 citations
- State-of-the-art review of soft computing applications in underground excavations (2020) — 442 citations
- Neural network and support vector machine models for the prediction of the liquefaction-induced uplift displacement of tunnels (2020) — 53 citations
- Assessment of pile drivability using random forest regression and multivariate adaptive regression splines (2019) — 187 citations
- Probabilistic stability analyses of undrained slopes by 3D random fields and finite element methods (2017) — 138 citations
- Influence of Particle Breakage on Critical State Line of Rockfill Material (2016) — 190 citations
- Multivariate adaptive regression splines and neural network models for prediction of pile drivability (2016) — 449 citations
- An improvement to MLR model for predicting liquefaction-induced lateral spread using multivariate adaptive regression splines (2014) — 120 citations
- Multivariate adaptive regression splines for analysis of geotechnical engineering systems (2013) — 335 citations