Yunhui Guo

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 6 papers; trusts 938 researchers and is trusted by 29 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 208 citations into it; #7,250 of 35,345 by trust here, #12,580 by citations
• Computer Vision and Pattern Recognition — 184 citations into it; #5,397 of 21,575 by trust here, #8,722 by citations
• Representation & Self-Supervised Learning — 93 citations into it
• Optimization & Training — 40 citations into it
• Efficiency & Compression — 37 citations into it
• Meta & Continual Learning — 35 citations into it; #562 of 1,041 by trust here, #409 by citations
• Model Architectures — 28 citations into it
• Evaluation & Benchmarks — 24 citations into it

Whom Yunhui Guo trusts overall

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

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

Who trusts Yunhui Guo overall

1. Fei Ye
2. Tailin Liang
3. Markus Nagel
4. By Lei Deng
5. Léopold Cambier
6. Adrian G. Bors
7. Lucas Prado Osco
8. Gobinda Saha
9. Jorge Armando Mendez Mendez
10. Franziska Boenisch

Papers from Yunhui Guo's perspective

Ordered by how strongly the researchers Yunhui Guo 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. Gradient-based learning applied to document recognition
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• SpotTune: Transfer Learning Through Adaptive Fine-Tuning (2018) — 517 citations
• A Survey on Methods and Theories of Quantized Neural Networks (2018) — 275 citations
• A Broader Study of Cross-Domain Few-Shot Learning (2019) — 423 citations
• A New Benchmark for Evaluation of Cross-Domain Few-Shot Learning (2019) — 45 citations
• Improved Schemes for Episodic Memory-based Lifelong Learning (2019) — 104 citations
• Learning with Long-term Remembering: Following the Lead of Mixed Stochastic Gradient (2019) — 10 citations