Haotao Wang

Computer Vision and Pattern Recognition · 2020

A researcher in the Science corpus: 3 papers; trusts 230 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Computer Vision and Pattern Recognition — 95 citations into it
• Artificial Intelligence — 82 citations into it
• Efficiency & Compression — 33 citations into it
• Safety, Robustness & Interpretability — 26 citations into it
• Model Architectures — 19 citations into it
• Optimization & Training — 10 citations into it
• Evaluation & Benchmarks — 8 citations into it

Whom Haotao Wang trusts overall

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

1. Alex Krizhevsky
2. Kaiming He
3. Geoffrey E. Hinton
4. Diederik P. Kingma
5. Christian Szegedy
6. Yoshua Bengio
7. Ilya Sutskever
8. Karen Simonyan
9. Yann LeCun
10. Dekang Lin

Who trusts Haotao Wang overall

1. Dan Hendrycks

Papers from Haotao Wang's perspective

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

Their papers

• I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively (2020) — 21 citations
• AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks (2020) — 92 citations
• Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference (2020) — 89 citations