Senzhang Wang
Building and Construction · 2015–2020
A researcher in the Science corpus: 6 papers; trusts 47 researchers and is trusted by 4 researchers here, as inferred from the citation graph.
Subfields
- Building and Construction — 52 citations into it; #2,729 of 2,909 by trust here, #1,369 by citations
- Transportation — 43 citations into it; #1,951 of 2,062 by trust here, #774 by citations
- Artificial Intelligence — 39 citations into it
- Management Science and Operations Research — 33 citations into it; #9,093 of 11,946 by trust here, #9,931 by citations
- Computer Vision and Pattern Recognition — 23 citations into it
- Signal Processing — 23 citations into it; #5,882 of 7,572 by trust here, #3,784 by citations
- Control and Systems Engineering — 21 citations into it
- Economics and Econometrics — 16 citations into it
Whom Senzhang Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yu Zheng
- Junbo Zhang
- Dirk Helbing
- Yoshua Bengio
- Geoffrey E. Hinton
- Alexandros Karatzoglou
- Dzmitry Bahdanau
- Ilya Sutskever
- Yaguang Li
- Jing Yuan
Who trusts Senzhang Wang overall
Papers from Senzhang Wang's perspective
Ordered by how strongly the researchers Senzhang Wang 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Attention is All you Need (Neural Information Processing Systems)
- Gradient-based learning applied to document recognition
- Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
Their papers
- Improving stock market prediction via heterogeneous information fusion (2017) — 193 citations
- Spatial temporal incidence dynamic graph neural networks for traffic flow forecasting (2020) — 315 citations
- Deep Learning for Spatio-Temporal Data Mining: A Survey (2019) — 797 citations
- Computing Urban Traffic Congestions by Incorporating Sparse GPS Probe Data and Social Media Data (2017) — 78 citations
- Estimating Urban Traffic Congestions with Multi-sourced Data (2016) — 38 citations
- Citywide traffic congestion estimation with social media (2015) — 72 citations