Zhengyao Jiang

Artificial Intelligence · 2016–2019

A researcher in the Science corpus: 3 papers; trusts 66 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 29 citations into it
• Reinforcement Learning — 23 citations into it
• Management Science and Operations Research — 15 citations into it; #4,205 of 11,946 by trust here, #6,024 by citations
• Optimization & Training — 11 citations into it
• Computer Vision and Pattern Recognition — 10 citations into it
• Economics and Econometrics — 7 citations into it
• Information Systems — 5 citations into it; #2,840 of 8,771 by trust here, #5,078 by citations
• Natural Language Processing — 5 citations into it

Whom Zhengyao Jiang trusts overall

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

1. Ronald J. Williams
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. John Schulman
5. Liang Bin
6. Paul J. Werbos
7. Sepp Hochreiter
8. Alex Krizhevsky
9. Jimmy Ba
10. Volodymyr Mnih

Who trusts Zhengyao Jiang overall

1. A. Ozbayoglu
2. M. Ugur Gudelek
3. Ben M. Hambly
4. Srijita Das
5. Nguyen Cong Luong
6. Omer Berat Sezer
7. Dixing Xu
8. Renyuan Xu
9. Sriraam Natarajan
10. Dinh Thai Hoang

Papers from Zhengyao Jiang's perspective

Ordered by how strongly the researchers Zhengyao Jiang 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
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

• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem (2017) — 471 citations
• Cryptocurrency portfolio management with deep reinforcement learning (2016) — 240 citations
• Neural Logic Reinforcement Learning (2019) — 92 citations