Fangzhou Mu

Computer Vision and Pattern Recognition · 2020–2023

A researcher in the Science corpus: 2 papers; trusts 259 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 93 citations into it; #13,527 of 21,575 by trust here, #12,689 by citations
• Generative Models — 52 citations into it
• Artificial Intelligence — 51 citations into it; #21,677 of 35,345 by trust here, #19,018 by citations
• Representation & Self-Supervised Learning — 20 citations into it
• Model Architectures — 10 citations into it
• Optimization & Training — 10 citations into it
• Scaling & Learning Theory — 9 citations into it
• Statistical and Nonlinear Physics — 8 citations into it

Whom Fangzhou Mu trusts overall

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

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

Who trusts Fangzhou Mu overall

1. Alessandro Achille
2. Aditya Golatkar
3. Hyounguk Shon
4. Avinash Ravichandran
5. Janghyeon Lee
6. Marzia Polito

Papers from Fangzhou Mu's perspective

Ordered by how strongly the researchers Fangzhou Mu 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. ImageNet: A large-scale hierarchical image database
5. Attention is All you Need (Neural Information Processing Systems)
6. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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

• GLIGEN: Open-Set Grounded Text-to-Image Generation (2023) — 965 citations
• Gradients as Features for Deep Representation Learning (2020) — 46 citations