Zhang-Wei Hong

Artificial Intelligence · 2017–2018

A researcher in the Science corpus: 4 papers; trusts 30 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 70 citations into it
• Reinforcement Learning — 54 citations into it
• Computer Vision and Pattern Recognition — 33 citations into it
• Management Science and Operations Research — 16 citations into it
• Optimization & Training — 9 citations into it
• Safety, Robustness & Interpretability — 9 citations into it

Whom Zhang-Wei Hong trusts overall

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

1. Diederik P. Kingma
2. Richard S. Sutton
3. Geoffrey E. Hinton
4. Ronald J. Williams
5. Alex Krizhevsky
6. Yoshua Bengio
7. Jimmy Ba
8. Ilya Sutskever
9. Sepp Hochreiter
10. Yann LeCun

Who trusts Zhang-Wei Hong overall

1. Julien Roy
2. Sriram Srinivasan
3. Andrea Tacchetti
4. Amal Feriani
5. Alex Bewley
6. A. Serban
7. Katja Auernhammer
8. Kenneth T. Co
9. Leo Gao

Papers from Zhang-Wei Hong's perspective

Ordered by how strongly the researchers Zhang-Wei Hong 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. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Attention is All you Need (Neural Information Processing Systems)
7. ImageNet: A large-scale hierarchical image database
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Tactics of Adversarial Attack on Deep Reinforcement Learning Agents (2017) — 474 citations
• A Deep Policy Inference Q-Network for Multi-Agent Systems (2017) — 93 citations
• Virtual-to-Real: Learning to Control in Visual Semantic Segmentation (2018) — 85 citations
• Diversity-Driven Exploration Strategy for Deep Reinforcement Learning (2018) — 147 citations