Yue Zhao

Artificial Intelligence · with Chen · 2018–2021

A researcher in the Science corpus: 4 papers; trusts 52 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 79 citations into it; #25,732 of 35,345 by trust here, #5,221 by citations
• Optimization & Training — 33 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it
• Safety, Robustness & Interpretability — 21 citations into it
• Signal Processing — 20 citations into it
• Federated & Distributed Learning — 17 citations into it; #236 of 319 by trust here, #53 by citations
• Computational Mechanics — 13 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Yue Zhao trusts overall

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

1. Alex Krizhevsky
2. Yann LeCun
3. Geoffrey E. Hinton
4. Ian J. Goodfellow
5. L. Eon Bottou
6. Yoshua Bengio
7. Kaiming He
8. Ilya Sutskever
9. H. Brendan McMahan
10. Diederik P. Kingma

Who trusts Yue Zhao overall

1. Tzu-Ming Harry Hsu
2. Jihong Park
3. Jiawen Kang
4. Filip Hanzely
5. Takayuki Nishio
6. Li Huang
7. Young Geun Kim
8. Daliang Li
9. Qiong Wu
10. Felix Sattler

Papers from Yue Zhao's perspective

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

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
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

• CommanderSong: A Systematic Approach for Practical Adversarial Voice Recognition (2018) — 399 citations
• Federated Learning with Non-IID Data (2018) — 3,343 citations
• Federated Learning Based on Dynamic Regularization (2021) — 1,176 citations
• DLFuzz: differential fuzzing testing of deep learning systems (2018) — 317 citations