Kai-Hung Chang

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 22 citations into it
• Computer Vision and Pattern Recognition — 19 citations into it
• Generative Models — 14 citations into it
• Graph Learning — 13 citations into it

Whom Kai-Hung Chang trusts overall

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

1. Thomas Kipf
2. Max Welling
3. Stephen P. Boyd
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Wengong Jin
7. Yoshua Bengio
8. Eric Jang
9. Alex Krizhevsky
10. Yann LeCun

Papers from Kai-Hung Chang's perspective

Ordered by how strongly the researchers Kai-Hung Chang trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
9. GENERATIVE ADVERSARIAL NETS
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• Learning to simulate and design for structural engineering (2020) — 67 citations
• House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation (2020) — 298 citations