Shibo Zhang
Control and Systems Engineering · 2019–2021
A researcher in the Science corpus: 3 papers; trusts 63 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Control and Systems Engineering — 90 citations into it
- Computer Vision and Pattern Recognition — 69 citations into it
- Artificial Intelligence — 64 citations into it
- Mechanical Engineering — 61 citations into it
- Mechanics of Materials — 27 citations into it
- Representation & Self-Supervised Learning — 13 citations into it
- Generative Models — 12 citations into it
- Model Architectures — 8 citations into it
Whom Shibo Zhang trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Yoshua Bengio
- Haidong Shao
- Geoffrey E. Hinton
- Dana H. Ballard
- J. Urgen Schmidhuber
- Richard S. Sutton
- Yann LeCun
- Chen Lu
- Alex Krizhevsky
- Diederik P. Kingma
Who trusts Shibo Zhang overall
Papers from Shibo Zhang's perspective
Ordered by how strongly the researchers Shibo Zhang 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
- Long Short-Term Memory
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- 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)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
- GENERATIVE ADVERSARIAL NETS
Their papers
- Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances (2021) — 527 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