Zengyi Li

Computer Vision and Pattern Recognition · 2019

A researcher in the Science corpus: 2 papers; trusts 287 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 64 citations into it; #16,904 of 21,575 by trust here, #19,310 by citations
• Artificial Intelligence — 49 citations into it; #27,690 of 35,345 by trust here, #30,347 by citations
• Generative Models — 42 citations into it; #1,554 of 1,956 by trust here, #1,635 by citations
• Probabilistic & Bayesian Methods — 40 citations into it; #1,283 of 1,525 by trust here, #1,136 by citations
• Signal Processing — 14 citations into it
• Optimization & Training — 12 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Model Architectures — 7 citations into it

Whom Zengyi Li trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Radford M. Neal
4. Alex Krizhevsky
5. Kaiming He
6. Ruslan Salakhutdinov
7. Yoshua Bengio
8. Yann LeCun
9. Pascal Vincent
10. Aapo Hyvärinen

Who trusts Zengyi Li overall

1. Arash Vahdat
2. Will Grathwohl
3. Tianyu Pang
4. Alexia Jolicoeur-Martineau
5. Karsten Kreis
6. Zahra Kadkhodaie
7. Kun Xu
8. Kuan-Chieh Jackson Wang
9. Jan Kautz

Papers from Zengyi Li's perspective

Ordered by how strongly the researchers Zengyi Li 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. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Learning Energy-Based Models in High-Dimensional Spaces with Multiscale Denoising-Score Matching (2019) — 34 citations
• Annealed Denoising Score Matching: Learning Energy-Based Models in High-Dimensional Spaces (2019) — 13 citations