Yang Chen

Artificial Intelligence · with Cui · 2018

A researcher in the Science corpus: 2 papers; trusts 53 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 40 citations into it; #9,476 of 35,345 by trust here, #12,761 by citations
• Cognitive Neuroscience — 32 citations into it
• Computer Vision and Pattern Recognition — 31 citations into it
• Representation & Self-Supervised Learning — 14 citations into it
• Neuroscience — 12 citations into it
• Model Architectures — 10 citations into it; #757 of 2,278 by trust here, #1,215 by citations
• Electrical and Electronic Engineering — 7 citations into it
• Optimization & Training — 7 citations into it

Whom Yang Chen trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Vinod Nair
4. Kaiming He
5. Yann LeCun
6. Herbert Jaeger
7. Earl K. Miller
8. Alex Krizhevsky
9. Xavier Glorot
10. Ian J. Goodfellow

Who trusts Yang Chen overall

1. Zixuan Ke
2. Bing Liu
3. Gyuhak Kim
4. Wenpeng Hu
5. Sen Lin
6. Hu Xu
7. Song Zhang
8. Diana Benavides Prado
9. Gehui Shen
10. Gobinda Saha

Papers from Yang Chen's perspective

Ordered by how strongly the researchers Yang Chen 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet Large Scale Visual Recognition Challenge
9. Learning Multiple Layers of Features from Tiny Images
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Continuous Learning of Context-dependent Processing in Neural Networks (2018) — 36 citations
• Continual learning of context-dependent processing in neural networks (2018) — 401 citations