Kuan Eeik Tan

Artificial Intelligence · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 9 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it
• Optimization & Training — 17 citations into it
• Information Systems — 14 citations into it
• Management Science and Operations Research — 9 citations into it

Whom Kuan Eeik Tan trusts overall

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

1. Diederik P. Kingma

Papers from Kuan Eeik Tan's perspective

Ordered by how strongly the researchers Kuan Eeik Tan trusts endorse each paper. Same corpus, a different order for every researcher.

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. ImageNet classification with deep convolutional neural networks
3. Gradient-based learning applied to document recognition
4. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
5. Long Short-Term Memory
6. Practical Bayesian Optimization of Machine Learning Algorithms
7. Learning Multiple Layers of Features from Tiny Images
8. ImageNet: A large-scale hierarchical image database
9. Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
10. Attention is All you Need (Neural Information Processing Systems)

Their papers

• Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System (2019) — 374 citations
• Federated Multi-view Matrix Factorization for Personalized Recommendations (2020) — 84 citations