Kaidi Cao

Computer Vision and Pattern Recognition · 2019–2020

A researcher in the Science corpus: 4 papers; trusts 299 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 75 citations into it
• Artificial Intelligence — 65 citations into it; #17,518 of 35,345 by trust here, #10,667 by citations
• Representation & Self-Supervised Learning — 15 citations into it
• Optimization & Training — 13 citations into it; #1,552 of 2,601 by trust here, #1,059 by citations
• Radiology, Nuclear Medicine and Imaging — 11 citations into it
• Model Architectures — 5 citations into it

Whom Kaidi 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. Diederik P. Kingma
6. Yann LeCun
7. Yoshua Bengio
8. L. Eon Bottou
9. Ilya Sutskever
10. Andrew Zisserman

Who trusts Kaidi Cao overall

1. Shiori Sagawa
2. Bingyi Kang
3. Hankook Lee
4. Yu Li
5. Zhisheng Zhong
6. Jiequan Cui
7. Yuzhe Yang
8. Yu Kong
9. Jiawei Ren
10. Tao Wang

Papers from Kaidi Cao's perspective

Ordered by how strongly the researchers Kaidi 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. ImageNet: A large-scale hierarchical image database
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. Long Short-Term Memory
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Going deeper with convolutions

Their papers

• Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss (2019) — 2,156 citations
• Few-Shot Video Classification via Temporal Alignment (2019) — 294 citations
• Coresets for Robust Training of Deep Neural Networks against Noisy Labels (2020) — 97 citations
• Rethinking Class-Balanced Methods for Long-Tailed Visual Recognition from a Domain Adaptation Perspective (2020)