Jian Li

Artificial Intelligence · with Ding · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 76 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 60 citations into it; #25,163 of 35,345 by trust here, #23,731 by citations
• Computer Vision and Pattern Recognition — 23 citations into it; #15,543 of 21,575 by trust here, #15,529 by citations
• Optimization & Training — 18 citations into it; #2,038 of 2,601 by trust here, #2,215 by citations
• Computational Mechanics — 15 citations into it
• Scaling & Learning Theory — 14 citations into it
• Evaluation & Benchmarks — 12 citations into it
• Prompting & Reasoning — 10 citations into it
• Statistical Learning Theory — 8 citations into it

Whom Jian Li trusts overall

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

1. Kaiming He
2. Geoffrey E. Hinton
3. Alex Krizhevsky
4. Yoshua Bengio
5. F. Clarke
6. Shai Shalev-Shwartz
7. Jason D. Lee
8. Diederik P. Kingma
9. Yann LeCun
10. Ilya Sutskever

Who trusts Jian Li overall

1. Lénaïc Chizat
2. Juntang Zhuang
3. Francis R. Bach

Papers from Jian Li's perspective

Ordered by how strongly the researchers Jian Li 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. Gradient-based learning applied to document recognition
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
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. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)

Their papers

• Gradient Descent Maximizes the Margin of Homogeneous Neural Networks (2019) — 417 citations
• FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding (2021) — 31 citations