Guang-He Lee

Artificial Intelligence · 2016–2018

A researcher in the Science corpus: 2 papers; trusts 18 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 16 citations into it
• Computer Vision and Pattern Recognition — 13 citations into it
• Optimization & Training — 10 citations into it
• Information Systems — 8 citations into it
• Management Science and Operations Research — 6 citations into it
• Probabilistic & Bayesian Methods — 5 citations into it
• Signal Processing — 5 citations into it

Whom Guang-He Lee trusts overall

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

1. Peter Filzmoser
2. Yoshua Bengio
3. Ruslan Salakhutdinov
4. Martin A. Riedmiller
5. John C. Duchi
6. Yehuda Koren
7. Tamara G. Kolda
8. Geoffrey E. Hinton
9. M. Pawan Kumar
10. Joonseok Lee

Papers from Guang-He Lee's perspective

Ordered by how strongly the researchers Guang-He Lee 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. Gradient-based learning applied to document recognition
5. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
7. Attention is All you Need (Neural Information Processing Systems)
8. Going deeper with convolutions
9. Learning Multiple Layers of Features from Tiny Images
10. GENERATIVE ADVERSARIAL NETS

Their papers

• ProbGAN: Towards Probabilistic GAN with Theoretical Guarantees (2018) — 32 citations
• Toward Implicit Sample Noise Modeling: Deviation-driven Matrix Factorization (2016)