Xihong Wu

Artificial Intelligence · with Li · 2004–2015

A researcher in the Science corpus: 5 papers; trusts 107 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 50 citations into it; #13,513 of 35,345 by trust here, #22,463 by citations
• Signal Processing — 30 citations into it; #2,565 of 7,572 by trust here, #4,373 by citations
• Natural Language Processing — 21 citations into it
• Speech & Audio — 17 citations into it
• Model Architectures — 15 citations into it
• Computer Vision and Pattern Recognition — 14 citations into it; #15,366 of 21,575 by trust here, #16,367 by citations
• Optimization & Training — 5 citations into it

Whom Xihong Wu trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. J. Urgen Schmidhuber
4. Yoshua Bengio
5. Razvan Pascanu
6. Hasim Sak
7. Tomas Mikolov
8. Sepp Hochreiter
9. Andrew W. Senior
10. Felix Alexander Gers

Who trusts Xihong Wu overall

1. Xiangang Li
2. Yajie Miao
3. Terrance Devries
4. Zhongdi Qu
5. Caifeng Shan
6. Florian Metze
7. Graham W. Taylor

Papers from Xihong Wu's perspective

Ordered by how strongly the researchers Xihong Wu trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Long Short-Term Memory
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Boosting Local Binary Pattern (LBP)-Based Face Recognition (2004) — 302 citations
• Constructing long short-term memory based deep recurrent neural networks for large vocabulary speech recognition (2014) — 324 citations
• Modeling speaker variability using long short-term memory networks for speech recognition (2015) — 25 citations
• Chinese syllable-to-character conversion with recurrent neural network based supervised sequence labelling (2015) — 6 citations
• Context dependent syllable acoustic model for continuous Chinese speech recognition (2007) — 22 citations