Tong Yu

Artificial Intelligence · 2 papers · 2020–2021

A researcher in the Science corpus: 2 papers; trusts 308 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 91 citations into it; #19,094 of 35,345 by trust here, #20,377 by citations
• Computer Vision and Pattern Recognition — 40 citations into it
• Optimization & Training — 40 citations into it; #1,634 of 2,601 by trust here, #2,378 by citations
• Computational Theory and Mathematics — 20 citations into it; #6,275 of 12,182 by trust here, #9,306 by citations
• Management Science and Operations Research — 17 citations into it
• Neural Architecture Search & AutoML — 17 citations into it; #160 of 283 by trust here, #270 by citations
• Model Architectures — 16 citations into it
• Large Language Models — 14 citations into it

Whom Tong Yu trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yann LeCun
5. Diederik P. Kingma
6. Ilya Sutskever
7. Frank Hutter
8. Kaiming He
9. Quoc V. Le
10. Matthew D. Zeiler

Who trusts Tong Yu overall

1. Gaurav Menghani
2. Kaiyi Ji
3. Junjie Yang
4. Yingbin Liang

Papers from Tong Yu's perspective

Ordered by how strongly the researchers Tong 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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

• Yuan 1.0: Large-Scale Pre-trained Language Model in Zero-Shot and Few-Shot Learning (2021) — 69 citations
• Hyper-Parameter Optimization: A Review of Algorithms and Applications (2020) — 690 citations