Cheng Lu

Computer Vision and Pattern Recognition · with Zhu · 2020–2023

A researcher in the Science corpus: 5 papers; trusts 279 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 131 citations into it; #13,353 of 21,575 by trust here, #3,765 by citations
• Generative Models — 111 citations into it; #1,640 of 1,956 by trust here, #610 by citations
• Artificial Intelligence — 71 citations into it
• Statistical and Nonlinear Physics — 50 citations into it
• Signal Processing — 31 citations into it; #6,496 of 7,572 by trust here, #4,900 by citations
• Probabilistic & Bayesian Methods — 28 citations into it
• Computational Mechanics — 23 citations into it
• Model Architectures — 15 citations into it

Whom Cheng Lu trusts overall

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

1. Diederik P. Kingma
2. Alex Krizhevsky
3. Geoffrey E. Hinton
4. Kaiming He
5. Yoshua Bengio
6. Jonathan Ho
7. Prafulla Dhariwal
8. Yann LeCun
9. Ilya Sutskever
10. Laurent Dinh

Who trusts Cheng Lu overall

1. Wenliang Zhao
2. Xingchao Liu
3. Hu Ye
4. Moritz Reuss
5. Tianwei Yin
6. Simian Luo
7. Lianghua Huang
8. Neta Shaul
9. Zhengyi Wang
10. Hanqun Cao

Papers from Cheng Lu's perspective

Ordered by how strongly the researchers Cheng Lu 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. Long Short-Term Memory
7. Gradient-based learning applied to document recognition
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. ImageNet Large Scale Visual Recognition Challenge

Their papers

• DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps (2022) — 2,469 citations
• DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models (2022) — 1,074 citations
• ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation (2023) — 1,425 citations
• VFlow: More Expressive Generative Flows with Variational Data Augmentation (2020) — 65 citations
• Implicit Normalizing Flows (2021) — 38 citations