Shengcao Cao

Artificial Intelligence · 2019–2023

A researcher in the Science corpus: 3 papers; trusts 329 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 101 citations into it; #27,734 of 35,345 by trust here, #12,387 by citations
• Computer Vision and Pattern Recognition — 85 citations into it; #16,995 of 21,575 by trust here, #8,612 by citations
• Model Architectures — 21 citations into it
• Optimization & Training — 15 citations into it
• Efficiency & Compression — 12 citations into it
• Neural Architecture Search & AutoML — 9 citations into it

Whom Shengcao Cao trusts overall

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

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

Who trusts Shengcao Cao overall

1. Martin Wistuba
2. Ambrish Rawat
3. Hao Zhang
4. Shilong Liu
5. Yaoyao Liu
6. Liam Li
7. Peng Gao
8. Feng Li

Papers from Shengcao Cao's perspective

Ordered by how strongly the researchers Shengcao Cao 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. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Rethinking Transformer-based Set Prediction for Object Detection (2020) — 383 citations
• Aligning Large Multimodal Models with Factually Augmented RLHF (2023) — 811 citations
• Learnable Embedding Space for Efficient Neural Architecture Compression (2019) — 45 citations