Lihui Wang
Industrial and Manufacturing Engineering · Engineering · 2015–2020
A researcher in the Science corpus: 7 papers; trusts 107 researchers and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Industrial and Manufacturing Engineering — 71 citations into it
- Computer Vision and Pattern Recognition — 41 citations into it
- Control and Systems Engineering — 34 citations into it; #7,805 of 8,257 by trust here, #6,764 by citations
- Artificial Intelligence — 32 citations into it
- Information Systems — 20 citations into it
- Mechanical Engineering — 17 citations into it
- Electrical and Electronic Engineering — 11 citations into it
- Safety, Risk, Reliability and Quality — 11 citations into it
Whom Lihui Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Simon S. Haykin
- J. Urgen Schmidhuber
- Robert E. Schapire
- Bernhard Schölkopf
- Fei Tao
- Fadel M. Adib
- Karen Simonyan
- Leif E. Peterson
- Xun Xu
- Yann LeCun
Who trusts Lihui Wang overall
Papers from Lihui Wang's perspective
Ordered by how strongly the researchers Lihui Wang trusts endorse each paper. Same corpus, a different order for every researcher.
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Gradient-based learning applied to document recognition
- Long Short-Term Memory
- ImageNet: A large-scale hierarchical image database
- Attention is All you Need (Neural Information Processing Systems)
- Learning Multiple Layers of Features from Tiny Images
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Going deeper with convolutions
Their papers
- Gesture recognition for human-robot collaboration: A review (2017) — 377 citations
- Smart manufacturing process and system automation – A critical review of the standards and envisioned scenarios (2020) — 445 citations
- Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019) — 1,147 citations
- Enabling technologies and tools for digital twin (2019) — 1,279 citations
- Cloud manufacturing: key characteristics and applications (2017) — 313 citations
- Cloud-enabled prognosis for manufacturing (2015) — 315 citations
- Cloud-based design and manufacturing: A new paradigm in digital manufacturing and design innovation (2015) — 599 citations