Hsiao-Yu Fish Tung

Computer Vision and Pattern Recognition · 2014–2018

A researcher in the Science corpus: 5 papers; trusts 593 researchers and is trusted by 72 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 98 citations into it; #3,609 of 21,575 by trust here, #5,711 by citations
• Artificial Intelligence — 55 citations into it; #33,688 of 35,345 by trust here, #15,866 by citations
• Reinforcement Learning — 24 citations into it
• Control and Systems Engineering — 20 citations into it; #1,793 of 8,257 by trust here, #1,475 by citations
• Evaluation & Benchmarks — 19 citations into it; #2,915 of 3,147 by trust here, #2,133 by citations
• Statistics and Probability — 17 citations into it
• Generative Models — 16 citations into it
• Natural Language Processing — 13 citations into it

Whom Hsiao-Yu Fish Tung trusts overall

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

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

Who trusts Hsiao-Yu Fish Tung overall

1. Muhammed Kocabas
2. Angjoo Kanazawa
3. Michael J. Black
4. Georgios Pavlakos
5. Nikos Kolotouros
6. Thiemo Alldieck
7. Jiefeng Li
8. Kevin Qinghong Lin
9. Julian Chibane
10. Gerard Pons-Moll

Papers from Hsiao-Yu Fish Tung's perspective

Ordered by how strongly the researchers Hsiao-Yu Fish Tung 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. ImageNet: A large-scale hierarchical image database
5. Long Short-Term Memory
6. Attention is All you Need (Neural Information Processing Systems)
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. Going deeper with convolutions

Their papers

• Self-supervised Learning of Motion Capture (2017) — 332 citations
• Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision (2017) — 163 citations
• Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy (2016) — 293 citations
• Reward Learning from Narrated Demonstrations (2018) — 31 citations
• Spectral Methods for Indian Buffet Process Inference (2014) — 16 citations