Tzu-Ming Harry Hsu

Computer Vision and Pattern Recognition · 2016–2020

A researcher in the Science corpus: 5 papers; trusts 510 researchers and is trusted by 24 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 124 citations into it; #6,390 of 21,575 by trust here, #6,371 by citations
• Artificial Intelligence — 91 citations into it; #8,980 of 35,345 by trust here, #10,319 by citations
• Evaluation & Benchmarks — 25 citations into it
• Federated & Distributed Learning — 21 citations into it; #63 of 319 by trust here, #158 by citations
• Representation & Self-Supervised Learning — 21 citations into it
• Optimization & Training — 15 citations into it
• Natural Language Processing — 14 citations into it
• Model Architectures — 10 citations into it

Whom Tzu-Ming Harry Hsu trusts overall

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

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

Who trusts Tzu-Ming Harry Hsu overall

1. Qinbin Li
2. Romy Mi Luo
3. Sai Praneeth Karimireddy
4. Bingsheng He
5. Zachary B. Charles
6. Sashank J. Reddi
7. Jianyu Wang
8. Om Thakkar
9. Tao Sun
10. Guodong Long

Papers from Tzu-Ming Harry Hsu's perspective

Ordered by how strongly the researchers Tzu-Ming Harry Hsu 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. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Going deeper with convolutions
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• 3D-Aware Scene Manipulation via Inverse Graphics (2018) — 92 citations
• Clinically Accurate Chest X-Ray Report Generation (2019) — 353 citations
• Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification (2019) — 1,651 citations
• Federated Visual Classification with Real-World Data Distribution (2020) — 248 citations
• Transfer Neural Trees for Heterogeneous Domain Adaptation (2016) — 93 citations