Jeffrey O. Zhang

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 3 papers; trusts 331 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 95 citations into it; #14,124 of 35,345 by trust here, #10,065 by citations
• Computer Vision and Pattern Recognition — 77 citations into it; #9,182 of 21,575 by trust here, #7,092 by citations
• Representation & Self-Supervised Learning — 37 citations into it
• Meta & Continual Learning — 20 citations into it; #636 of 1,041 by trust here, #361 by citations
• Optimization & Training — 17 citations into it
• Efficiency & Compression — 10 citations into it; #708 of 1,264 by trust here, #613 by citations
• Model Architectures — 10 citations into it
• Statistical and Nonlinear Physics — 7 citations into it

Whom Jeffrey O. Zhang trusts overall

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

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

Who trusts Jeffrey O. Zhang overall

1. Yi-Lin Sung
2. Liyuan Wang
3. Nikhil Mehta
4. Jaemin Cho
5. Lvmin Zhang
6. Kevin J Liang
7. Vinay Kumar Verma
8. Yogesh Kumar Balaji
9. Da-Wei Zhou
10. Xingxing Zhang

Papers from Jeffrey O. Zhang's perspective

Ordered by how strongly the researchers Jeffrey O. Zhang 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. Gradient-based learning applied to document recognition
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. Learning Multiple Layers of Features from Tiny Images

Their papers

• Side-Tuning: A Baseline for Network Adaptation via Additive Side Networks (2019) — 298 citations
• Memory-Efficient Incremental Learning Through Feature Adaptation (2020) — 207 citations
• Side-Tuning: Network Adaptation via Additive Side Networks (2019) — 29 citations