Yulai Cong

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 3 papers; trusts 803 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 160 citations into it
• Artificial Intelligence — 121 citations into it
• Generative Models — 80 citations into it; #1,016 of 1,956 by trust here, #1,221 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Natural Language Processing — 15 citations into it
• Radiology, Nuclear Medicine and Imaging — 14 citations into it
• Signal Processing — 14 citations into it
• Meta & Continual Learning — 13 citations into it

Whom Yulai Cong trusts overall

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

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

Who trusts Yulai Cong overall

1. Juwon Seo
2. Liyuan Wang
3. Pei-Hung Chen
4. Qiankun Gao
5. Jingwen Kang
6. Ta-Chu Kao
7. Duo Li
8. Junsu Kim
9. Jihwan Bang
10. James Smith

Papers from Yulai Cong's perspective

Ordered by how strongly the researchers Yulai Cong 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. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• GAN Memory with No Forgetting (2020) — 151 citations
• On Leveraging Pretrained GANs for Generation with Limited Data (2020) — 102 citations
• On Leveraging Pretrained GANs for Limited-Data Generation (2020) — 22 citations