Bo-Hao Chang

Artificial Intelligence · 2017–2019

A researcher in the Science corpus: 3 papers; trusts 59 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 53 citations into it; #21,316 of 35,345 by trust here, #13,544 by citations
• Computer Vision and Pattern Recognition — 49 citations into it; #13,276 of 21,575 by trust here, #9,305 by citations
• Model Architectures — 33 citations into it; #1,459 of 2,278 by trust here, #1,191 by citations
• Optimization & Training — 18 citations into it
• Scaling & Learning Theory — 14 citations into it
• Statistical and Nonlinear Physics — 13 citations into it
• Representation & Self-Supervised Learning — 8 citations into it

Whom Bo-Hao Chang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Ross B. Girshick
6. Yann LeCun
7. Diederik P. Kingma
8. X. Zhang
9. Karen Simonyan
10. Ilya Sutskever

Who trusts Bo-Hao Chang overall

1. Xuechen Li
2. Yiping Lu
3. Lars Ruthotto
4. Zichao Long
5. A. Queiruga
6. Tong Qin
7. Linfeng Zhang
8. Eldad Haber
9. David Kristjanson Duvenaud
10. Jacob Kelly

Papers from Bo-Hao Chang's perspective

Ordered by how strongly the researchers Bo-Hao 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. 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• Reversible Architectures for Arbitrarily Deep Residual Neural Networks (2017) — 298 citations
• Multi-level Residual Networks from Dynamical Systems View (2017) — 184 citations
• Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs (2019) — 44 citations