Yaodong Yu

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 5 papers; trusts 115 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 158 citations into it; #11,676 of 35,345 by trust here, #5,359 by citations
• Computer Vision and Pattern Recognition — 88 citations into it; #18,395 of 21,575 by trust here, #14,151 by citations
• Safety, Robustness & Interpretability — 70 citations into it; #960 of 2,251 by trust here, #500 by citations
• Model Architectures — 36 citations into it
• Optimization & Training — 32 citations into it; #2,577 of 2,601 by trust here, #1,942 by citations
• Representation & Self-Supervised Learning — 26 citations into it
• Computational Mechanics — 22 citations into it
• Scaling & Learning Theory — 19 citations into it

Whom Yaodong Yu trusts overall

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

1. Alex Krizhevsky
2. Geoffrey E. Hinton
3. Kaiming He
4. Yann LeCun
5. Yoshua Bengio
6. Ilya Sutskever
7. Diederik P. Kingma
8. Ross B. Girshick
9. Christian Szegedy
10. Ian J. Goodfellow

Who trusts Yaodong Yu overall

1. Francesco Croce
2. Kyungho Baek
3. Sven Gowal
4. Tianyu Pang
5. Matthias Hein
6. Micah Goldblum
7. Tianyuan Zhang
8. Sylvestre-Alvise Rebuffi
9. Tianlong Chen
10. Aditi Raghunathan

Papers from Yaodong Yu's perspective

Ordered by how strongly the researchers Yaodong Yu 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Gradient-based learning applied to document recognition
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Learning Multiple Layers of Features from Tiny Images
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Theoretically Principled Trade-off between Robustness and Accuracy (2019) — 3,092 citations
• Learning One-hidden-layer ReLU Networks via Gradient Descent (2018) — 140 citations
• Data Poisoning Attacks on Multi-Task Relationship Learning (2018) — 46 citations
• Deep Networks from the Principle of Rate Reduction (2020) — 22 citations
• Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction (2020) — 251 citations