Hao Wu

Media Technology · with Li · 2013–2018

A researcher in the Science corpus: 2 papers; trusts 54 researchers and is trusted by 7 researchers here, as inferred from the citation graph.

Subfields

• Media Technology — 24 citations into it; #1,458 of 3,621 by trust here, #1,646 by citations
• Computer Vision and Pattern Recognition — 21 citations into it
• Artificial Intelligence — 17 citations into it; #16,869 of 35,345 by trust here, #20,836 by citations
• Analytical Chemistry — 10 citations into it
• Atmospheric Science — 10 citations into it
• Representation & Self-Supervised Learning — 10 citations into it
• Signal Processing — 10 citations into it
• Model Architectures — 9 citations into it

Whom Hao Wu trusts overall

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

1. Radford M. Neal
2. Paul J. Werbos
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Michael I. Jordan
6. Alex Graves
7. Zoubin Ghahramani
8. Alex Krizhevsky
9. Ilya Sutskever
10. Xiaojin Zhu

Who trusts Hao Wu overall

1. Nicolas Audebert
2. Nassim Ait Ali Braham
3. Shutao Li
4. Daifeng Peng
5. Behnood Rasti
6. Muhammad Ahmad
7. Danfeng Hong
8. Bertrand Le Saux
9. Jocelyn Chanussot
10. Lichao Mou

Papers from Hao Wu's perspective

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

Their papers

• Semi-Supervised Deep Learning Using Pseudo Labels for Hyperspectral Image Classification (2018) — 388 citations
• Manifold Regularized Sparse NMF for Hyperspectral Unmixing (2013) — 385 citations