Kai Zhong

Artificial Intelligence · 2016–2021

A researcher in the Science corpus: 4 papers; trusts 13 researchers and is trusted by 15 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 65 citations into it; #8,332 of 35,345 by trust here, #9,760 by citations
• Optimization & Training — 26 citations into it
• Computational Mechanics — 21 citations into it
• Scaling & Learning Theory — 16 citations into it
• Computational Mathematics — 13 citations into it; #330 of 612 by trust here, #473 by citations
• Statistical Learning Theory — 13 citations into it; #563 of 1,038 by trust here, #349 by citations

Whom Kai Zhong trusts overall

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

1. Peter L. Bartlett
2. Yoshua Bengio
3. Geoffrey E. Hinton
4. Anima Anandkumar
5. Alex Krizhevsky
6. Yann LeCun
7. Sanjeev Arora
8. Diederik P. Kingma
9. Moritz Hardt
10. Andrew Zisserman

Who trusts Kai Zhong overall

1. Simon Shaolei Du
2. Samet Oymak
3. Zeyuan Allen-Zhu
4. Jason D. Lee
5. Yuanzhi Li
6. M. Soltanolkotabi
7. Lili Su
8. Prateek Jain
9. Yuejie Chi
10. Xiao Zhang

Papers from Kai Zhong's perspective

Ordered by how strongly the researchers Kai Zhong 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Recovery Guarantees for One-hidden-layer Neural Networks (2017) — 349 citations
• Machine Learning for Electronic Design Automation: A Survey (2021) — 346 citations
• Learning Non-overlapping Convolutional Neural Networks with Multiple Kernels (2017) — 75 citations
• Dual Decomposed Learning with Factorwise Oracle for Structural SVM of Large Output Domain (2016) — 9 citations