Shao-Hua Sun

Artificial Intelligence · 2018–2022

A researcher in the Science corpus: 7 papers; trusts 691 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 182 citations into it; #30,168 of 35,345 by trust here, #18,186 by citations
• Computer Vision and Pattern Recognition — 128 citations into it; #14,118 of 21,575 by trust here, #10,154 by citations
• Reinforcement Learning — 101 citations into it
• Generative Models — 50 citations into it
• Control and Systems Engineering — 36 citations into it
• Natural Language Processing — 32 citations into it
• Representation & Self-Supervised Learning — 30 citations into it
• Information Systems — 21 citations into it

Whom Shao-Hua Sun trusts overall

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

1. Diederik P. Kingma
2. Geoffrey E. Hinton
3. Ilya Sutskever
4. Yoshua Bengio
5. Alex Krizhevsky
6. Alex Graves
7. Kaiming He
8. Yann LeCun
9. Sergey Levine
10. Oriol Vinyals

Who trusts Shao-Hua Sun overall

1. Dweep Trivedi
2. Han-Jia Ye
3. Xinyun Chen
4. Joseph J. Lim
5. Hongyu Ren
6. Risto Vuorio
7. Jesse Zhang
8. Youngwoon Lee
9. Ayan Kumar Bhunia
10. Hanjun Dai

Papers from Shao-Hua Sun's perspective

Ordered by how strongly the researchers Shao-Hua Sun 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. ImageNet Large Scale Visual Recognition Challenge
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Neural Program Synthesis from Diverse Demonstration Videos (2018) — 81 citations
• Learning to Synthesize Programs as Interpretable and Generalizable Policies (2021) — 96 citations
• Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation (2019) — 251 citations
• Feedback Adversarial Learning: Spatial Feedback for Improving Generative Adversarial Networks (2019) — 26 citations
• Skill-based Meta-Reinforcement Learning (2022) — 57 citations
• Composing Complex Skills by Learning Transition Policies (2018) — 103 citations
• Model-Agnostic Meta-Learning for Multimodal Task Distributions (2018)