Xiangning Chen

Artificial Intelligence · with Hsieh · 2021–2023

A researcher in the Science corpus: 2 papers; trusts 311 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 110 citations into it; #21,771 of 35,345 by trust here, #16,771 by citations
• Computer Vision and Pattern Recognition — 86 citations into it; #13,485 of 21,575 by trust here, #11,286 by citations
• Optimization & Training — 43 citations into it
• Evaluation & Benchmarks — 28 citations into it
• Model Architectures — 25 citations into it
• Representation & Self-Supervised Learning — 13 citations into it
• Large Language Models — 11 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Xiangning Chen trusts overall

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

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

Who trusts Xiangning Chen overall

1. Jean Kaddour
2. Jing Liu
3. Timothée Darcet
4. Momin Abbas
5. Jeremy M. Cohen
6. Francesco Pelosin
7. Mengzhou Xia
8. Jianfei Cai
9. Ricardo M. A. Silva
10. Mitchell Wortsman

Papers from Xiangning Chen's perspective

Ordered by how strongly the researchers Xiangning Chen 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations (2021) — 396 citations
• Symbolic Discovery of Optimization Algorithms (2023) — 679 citations