Kok-Lim Alvin Yau

Artificial Intelligence · 2013–2020

A researcher in the Science corpus: 4 papers; trusts 11 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 98 citations into it
• Computer Networks and Communications — 94 citations into it; #7,979 of 9,886 by trust here, #6,217 by citations
• Electrical and Electronic Engineering — 43 citations into it
• Computer Vision and Pattern Recognition — 18 citations into it
• Optimization & Training — 7 citations into it
• Probabilistic & Bayesian Methods — 7 citations into it
• Reinforcement Learning — 7 citations into it
• Generative Models — 5 citations into it

Whom Kok-Lim Alvin Yau trusts overall

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

1. Richard S. Sutton
2. Andrew Barto
3. Ejaz Ahmed
4. Geoffrey E. Hinton
5. J. Snyman
6. Yoshua Bengio
7. Ruslan Salakhutdinov
8. Michael H. Bowling
9. Alex Krizhevsky
10. J. Urgen Schmidhuber

Who trusts Kok-Lim Alvin Yau overall

1. Assaf Hallak

Papers from Kok-Lim Alvin Yau's perspective

Ordered by how strongly the researchers Kok-Lim Alvin Yau 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. Gradient-based learning applied to document recognition
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. GENERATIVE ADVERSARIAL NETS
6. Long Short-Term Memory
7. Learning Multiple Layers of Features from Tiny Images
8. Attention is All you Need (Neural Information Processing Systems)
9. Reinforcement Learning: An Introduction
10. ImageNet: A large-scale hierarchical image database

Their papers

• Federated Learning for Vehicular Internet of Things: Recent Advances and Open Issues (2020) — 333 citations
• Edge Computing in 5G: A Review (2019) — 297 citations
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017) — 433 citations
• Application of reinforcement learning to routing in distributed wireless networks: a review (2013) — 102 citations