Chengzhu Yu

Artificial Intelligence · 2015–2020

A researcher in the Science corpus: 8 papers; trusts 89 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 154 citations into it; #10,763 of 35,345 by trust here, #6,422 by citations
• Signal Processing — 99 citations into it; #2,713 of 7,572 by trust here, #1,170 by citations
• Speech & Audio — 90 citations into it; #885 of 1,076 by trust here, #561 by citations
• Natural Language Processing — 84 citations into it
• Model Architectures — 40 citations into it; #2,067 of 2,278 by trust here, #2,215 by citations
• Computer Vision and Pattern Recognition — 28 citations into it
• Optimization & Training — 9 citations into it; #2,543 of 2,601 by trust here, #2,428 by citations
• Probabilistic & Bayesian Methods — 5 citations into it

Whom Chengzhu Yu trusts overall

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

1. Alex Graves
2. Geoffrey E. Hinton
3. Heiga Zen
4. Oriol Vinyals
5. Yoshua Bengio
6. Diederik P. Kingma
7. Ilya Sutskever
8. Alan W. Black
9. Alex Krizhevsky
10. Daniel Povey

Who trusts Chengzhu Yu overall

1. Jinyu Li
2. Qiao Tian
3. D. Lim
4. Daxin Tan
5. Zewang Zhang
6. Songxiang Liu
7. Xu Tan
8. Rohit Prabhavalkar
9. Yanli Li
10. Zhaoxi Mu

Papers from Chengzhu Yu's perspective

Ordered by how strongly the researchers Chengzhu Yu 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. ImageNet: A large-scale hierarchical image database
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Going deeper with convolutions

Their papers

• The NTT CHiME-3 system: Advances in speech enhancement and recognition for mobile multi-microphone devices (2015) — 250 citations
• Advances in all-neural speech recognition (2016) — 96 citations
• Minimum Bayes Risk Training of RNN-Transducer for End-to-End Speech Recognition (2019) — 40 citations
• Improving Attention Based Sequence-to-Sequence Models for End-to-End English Conversational Speech Recognition (2018) — 59 citations
• Improving Attention-Based End-to-End ASR Systems with Sequence-Based Loss Functions (2018) — 10 citations
• Unsupervised Speech Recognition via Segmental Empirical Output Distribution Matching (2018) — 42 citations
• DurIAN: Duration Informed Attention Network For Multimodal Synthesis (2019) — 119 citations
• DurIAN: Duration Informed Attention Network for Speech Synthesis (2020) — 80 citations