Xin Dong

Computer Vision and Pattern Recognition · with Kautz · 2019–2021

A researcher in the Science corpus: 2 papers; trusts 219 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 71 citations into it; #11,425 of 21,575 by trust here, #18,782 by citations
• Artificial Intelligence — 60 citations into it; #18,145 of 35,345 by trust here, #29,412 by citations
• Efficiency & Compression — 15 citations into it
• Model Architectures — 14 citations into it
• Optimization & Training — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Safety, Robustness & Interpretability — 10 citations into it

Whom Xin Dong trusts overall

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

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

Who trusts Xin Dong overall

1. Jing Liu
2. Minsik Cho
3. Zechun Liu

Papers from Xin Dong's perspective

Ordered by how strongly the researchers Xin Dong 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. ImageNet: A large-scale hierarchical image database
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. Learning Multiple Layers of Features from Tiny Images
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Additive Powers-of-Two Quantization: An Efficient Non-uniform Discretization for Neural Networks (2019) — 334 citations
• Deep Neural Networks are Surprisingly Reversible: A Baseline for Zero-Shot Inversion (2021) — 11 citations