Tongzhou Wang

Computer Vision and Pattern Recognition · with Torralba · 2018–2022

A researcher in the Science corpus: 5 papers; trusts 848 researchers and is trusted by 68 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 189 citations into it; #4,104 of 21,575 by trust here, #5,565 by citations
• Artificial Intelligence — 113 citations into it; #4,924 of 35,345 by trust here, #5,600 by citations
• Generative Models — 69 citations into it; #1,385 of 1,956 by trust here, #1,134 by citations
• Representation & Self-Supervised Learning — 44 citations into it; #784 of 3,145 by trust here, #1,172 by citations
• Optimization & Training — 29 citations into it
• Evaluation & Benchmarks — 23 citations into it
• Computer Graphics and Computer-Aided Design — 18 citations into it
• Signal Processing — 17 citations into it; #5,767 of 7,572 by trust here, #3,309 by citations

Whom Tongzhou Wang trusts overall

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

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

Who trusts Tongzhou Wang overall

1. Junliang Yu
2. Eric Wallace
3. Feng Wang
4. Paul Micaelli
5. Minyoung Huh
6. Tianyu Gao
7. Joshua Robinson
8. Han-Jia Ye
9. Timothy M. Hospedales
10. Alaaeldin El-Nouby

Papers from Tongzhou Wang's perspective

Ordered by how strongly the researchers Tongzhou Wang 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. 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. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere (2020) — 2,520 citations
• Diverse Image Generation via Self-Conditioned GANs (2020) — 122 citations
• Dataset Distillation by Matching Training Trajectories (2022) — 606 citations
• Rewriting a Deep Generative Model (2020) — 162 citations
• Dataset Distillation (2018) — 376 citations