Miao Zhang

Artificial Intelligence · with Pan · 2020–2022

A researcher in the Science corpus: 4 papers; trusts 238 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it
• Computer Vision and Pattern Recognition — 109 citations into it
• Neural Architecture Search & AutoML — 49 citations into it
• Model Architectures — 21 citations into it
• Optimization & Training — 17 citations into it
• Evaluation & Benchmarks — 15 citations into it
• Molecular Biology — 11 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Miao Zhang trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Xuanyi Dong
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Kaiming He
6. Karen Simonyan
7. Diederik P. Kingma
8. Quoc V. Le
9. Yann LeCun
10. Ilya Sutskever

Who trusts Miao Zhang overall

1. Changlin Li
2. Pengzhen Ren
3. Yan Zhang
4. Siyuan Liang
5. Xiaojun Chang
6. Yun Xiao

Papers from Miao Zhang's perspective

Ordered by how strongly the researchers Miao Zhang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• iDARTS: Differentiable Architecture Search with Stochastic Implicit Gradients (2021) — 85 citations
• Graph Neural Networks for Graphs With Heterophily: A Survey (2022) — 315 citations
• One-Shot Neural Architecture Search: Maximising Diversity to Overcome Catastrophic Forgetting (2020) — 81 citations
• Overcoming Multi-Model Forgetting in One-Shot NAS With Diversity Maximization (2020) — 89 citations