Cheng-Hao Ho

Artificial Intelligence · 2019

A researcher in the Science corpus: 2 papers; trusts 66 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 52 citations into it; #13,609 of 35,345 by trust here, #12,974 by citations
• Computer Vision and Pattern Recognition — 21 citations into it; #8,898 of 21,575 by trust here, #8,952 by citations
• Meta & Continual Learning — 16 citations into it; #372 of 1,041 by trust here, #369 by citations
• Natural Language Processing — 16 citations into it
• Representation & Self-Supervised Learning — 14 citations into it

Whom Cheng-Hao Ho trusts overall

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

1. Alec Radford
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Ronald Kemker
6. Alex Krizhevsky
7. Sungjin Lee
8. Mohamed Elhoseiny
9. Diederik P. Kingma
10. Rahaf Aljundi

Who trusts Cheng-Hao Ho overall

1. Yannis Papanikolaou
2. Zixuan Ke
3. Yanzhe Zhang
4. Nithin Holla
5. Andrea Pierleoni
6. Angeliki Lazaridou
7. Aman Hussain
8. Yung-Sung Chuang
9. Zirui Wang
10. Jingyuan Sun

Papers from Cheng-Hao Ho's perspective

Ordered by how strongly the researchers Cheng-Hao Ho 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. ImageNet Large Scale Visual Recognition Challenge
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• LAMOL: LAnguage MOdeling for Lifelong Language Learning (2019) — 253 citations
• LAMAL: LAnguage Modeling Is All You Need for Lifelong Language Learning (2019) — 39 citations