Bingnan Wang
Control and Systems Engineering · arXiv.org · 2019
A researcher in the Science corpus: 3 papers; trusts 54 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Control and Systems Engineering — 89 citations into it
- Mechanical Engineering — 60 citations into it
- Artificial Intelligence — 40 citations into it
- Mechanics of Materials — 27 citations into it
- Computer Vision and Pattern Recognition — 16 citations into it
- Representation & Self-Supervised Learning — 11 citations into it
- Industrial and Manufacturing Engineering — 6 citations into it
- Civil and Structural Engineering — 5 citations into it
Whom Bingnan Wang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Haidong Shao
- Yoshua Bengio
- Dana H. Ballard
- Chen Lu
- J. Urgen Schmidhuber
- Yaguo Lei
- Yann LeCun
- Hongkai Jiang
- Alec Radford
- Geoffrey E. Hinton
Who trusts Bingnan Wang overall
Papers from Bingnan Wang's perspective
Ordered by how strongly the researchers Bingnan 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)
- Gradient-based learning applied to document recognition
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- Long Short-Term Memory
- Attention is All you Need (Neural Information Processing Systems)
- GENERATIVE ADVERSARIAL NETS
- 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
- Deep Neural Network Inverse Design of Integrated Photonic Power Splitters (2019) — 318 citations
- Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review (2019) — 477 citations
- Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review (2019) — 330 citations