Shih-Yang Su

Computer Vision and Pattern Recognition · 2017–2021

A researcher in the Science corpus: 5 papers; trusts 437 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 125 citations into it
• Artificial Intelligence — 61 citations into it
• Reinforcement Learning — 50 citations into it
• Computer Graphics and Computer-Aided Design — 24 citations into it
• Computational Mechanics — 21 citations into it
• Management Science and Operations Research — 17 citations into it
• Generative Models — 15 citations into it
• Control and Systems Engineering — 13 citations into it

Whom Shih-Yang Su trusts overall

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

1. Kaiming He
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Alex Krizhevsky
5. Karen Simonyan
6. Yoshua Bengio
7. Yann LeCun
8. Ross B. Girshick
9. Andrew Zisserman
10. Jimmy Ba

Who trusts Shih-Yang Su overall

1. Matthew Tancik
2. Zhengqi Li
3. Lukas Höllein

Papers from Shih-Yang Su's perspective

Ordered by how strongly the researchers Shih-Yang Su 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. ImageNet: A large-scale hierarchical image database
6. Long Short-Term Memory
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

• 3D Photography Using Context-Aware Layered Depth Inpainting (2020) — 342 citations
• A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose (2021) — 311 citations
• A Deep Policy Inference Q-Network for Multi-Agent Systems (2017) — 93 citations
• Virtual-to-Real: Learning to Control in Visual Semantic Segmentation (2018) — 85 citations
• Diversity-Driven Exploration Strategy for Deep Reinforcement Learning (2018) — 147 citations