Hankui Zhuo

Artificial Intelligence · 2014–2019

A researcher in the Science corpus: 6 papers; trusts 68 researchers and is trusted by 3 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 89 citations into it
• Natural Language Processing — 28 citations into it; #4,635 of 5,688 by trust here, #5,412 by citations
• Reinforcement Learning — 27 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Optimization & Training — 12 citations into it
• Computer Networks and Communications — 9 citations into it
• Representation & Self-Supervised Learning — 9 citations into it
• Federated & Distributed Learning — 8 citations into it

Whom Hankui Zhuo trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. H. Brendan McMahan
5. Alex Krizhevsky
6. Matthew E. Taylor
7. Richard S. Sutton
8. Ilya Sutskever
9. Yann LeCun
10. Volodymyr Mnih

Who trusts Hankui Zhuo overall

1. Ankuj Arora
2. Sawsan AbdulRahman
3. Xiang Li
4. Humbert Fiorino
5. Tuan Anh Nguyen

Papers from Hankui Zhuo's perspective

Ordered by how strongly the researchers Hankui Zhuo 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. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Federated Hierarchical Hybrid Networks for Clickbait Detection (2019) — 5 citations
• Federated Deep Reinforcement Learning (2019) — 106 citations
• Federated Reinforcement Learning (2019) — 84 citations
• Multi-Matching Network for Multiple Choice Reading Comprehension (2019) — 45 citations
• Plan explicability and predictability for robot task planning (2015) — 184 citations
• Action-model acquisition for planning via transfer learning (2014) — 96 citations