Chudi Zhong

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 3 papers; trusts 100 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 181 citations into it; #33,272 of 35,345 by trust here, #20,836 by citations
• Safety, Robustness & Interpretability — 70 citations into it
• Computer Vision and Pattern Recognition — 67 citations into it
• Statistical Learning Theory — 42 citations into it
• Algorithms & Complexity — 36 citations into it; #506 of 516 by trust here, #380 by citations
• Representation & Self-Supervised Learning — 36 citations into it
• Statistics and Probability — 33 citations into it
• Medical & Health AI — 27 citations into it

Whom Chudi Zhong trusts overall

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

1. Cynthia Rudin
2. J. Ross Quinlan
3. Geoffrey E. Hinton
4. Berk Ustun
5. Yoshua Bengio
6. Leo Breiman
7. Alex Krizhevsky
8. Niko Speybroeck
9. Diederik P. Kingma
10. Yann LeCun

Who trusts Chudi Zhong overall

1. Cynthia Rudin
2. Vinícius G. Costa
3. Jiayun Dong
4. Zhi Chen
5. Yi Zhu
6. Emir Demirovi'c
7. Emilio Carrizosa

Papers from Chudi Zhong's perspective

Ordered by how strongly the researchers Chudi Zhong 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
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

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021) — 986 citations
• Generalized and Scalable Optimal Sparse Decision Trees (2020) — 178 citations
• Generalized Optimal Sparse Decision Trees (2020) — 5 citations