Wayne Xiong

Artificial Intelligence · 2016–2017

A researcher in the Science corpus: 5 papers; trusts 71 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 120 citations into it; #14,331 of 35,345 by trust here, #5,002 by citations
• Model Architectures — 65 citations into it
• Signal Processing — 61 citations into it; #2,799 of 7,572 by trust here, #482 by citations
• Speech & Audio — 59 citations into it; #859 of 1,076 by trust here, #308 by citations
• Natural Language Processing — 51 citations into it
• Computer Vision and Pattern Recognition — 35 citations into it
• Optimization & Training — 20 citations into it
• Efficiency & Compression — 6 citations into it

Whom Wayne Xiong trusts overall

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

1. Dong Yu
2. Andreas Stolcke
3. Tomas Mikolov
4. Geoffrey E. Hinton
5. Frank Torsten Bernd Seide
6. Alex Graves
7. Ilya Sutskever
8. Yoshua Bengio
9. Hasim Sak
10. Yann LeCun

Who trusts Wayne Xiong overall

1. Dong Yu
2. Jasha Droppo
3. Andreas Stolcke
4. Hairong Liu
5. Kartik Audhkhasi
6. Xuejing Yuan
7. Naoyuki Kanda
8. Xuedong Huang
9. Amr El-Desoky Mousa
10. Oleksii Kuchaiev

Papers from Wayne Xiong's perspective

Ordered by how strongly the researchers Wayne Xiong 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. Long Short-Term Memory
5. Attention is All you Need (Neural Information Processing Systems)
6. Gradient-based learning applied to document recognition
7. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
8. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Achieving Human Parity in Conversational Speech Recognition (2016) — 594 citations
• The microsoft 2016 conversational speech recognition system (2016) — 292 citations
• The Microsoft 2017 Conversational Speech Recognition System (2017) — 490 citations
• Deep Convolutional Neural Networks with Layer-Wise Context Expansion and Attention (2016) — 80 citations
• Toward Human Parity in Conversational Speech Recognition (2017) — 200 citations