Jiechuan Jiang

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 111 researchers and is trusted by 19 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 67 citations into it; #8,635 of 35,345 by trust here, #11,327 by citations
• Reinforcement Learning — 52 citations into it; #808 of 2,242 by trust here, #1,241 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Computational Theory and Mathematics — 12 citations into it
• LLM Agents & Tool Use — 12 citations into it; #23 of 124 by trust here, #50 by citations
• Computer Science Applications — 9 citations into it
• Computer Networks and Communications — 8 citations into it; #1,893 of 9,886 by trust here, #2,539 by citations
• Management Science and Operations Research — 8 citations into it

Whom Jiechuan Jiang trusts overall

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

1. Jakob N. Foerster
2. Geoffrey E. Hinton
3. Pieter Abbeel
4. Tuomas Haarnoja
5. Volodymyr Mnih
6. Diederik P. Kingma
7. Yoshua Bengio
8. Richard S. Sutton
9. Alex Krizhevsky
10. Ryan J. Lowe

Who trusts Jiechuan Jiang overall

1. Afshin Oroojlooyjadid
2. Davood Hajinezhad
3. Yuhang Zhao
4. Yongyuan Liang
5. Akshat Agarwal
6. Heechang Ryu
7. Yen-Cheng Liu
8. Kyunghwan Son
9. Chenghao Li
10. Wendelin Böhmer

Papers from Jiechuan Jiang's perspective

Ordered by how strongly the researchers Jiechuan Jiang 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. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learning Attentional Communication for Multi-Agent Cooperation (2018) — 609 citations
• The Emergence of Individuality in Multi-Agent Reinforcement Learning (2020) — 5 citations
• Graph Convolutional Reinforcement Learning (2018) — 435 citations
• Graph Convolutional Reinforcement Learning for Multi-Agent Cooperation (2018) — 15 citations