Naihan Li

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 4 papers; trusts 52 researchers and is trusted by 43 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 62 citations into it; #4,679 of 35,345 by trust here, #4,336 by citations
• Speech & Audio — 47 citations into it; #242 of 1,076 by trust here, #220 by citations
• Natural Language Processing — 45 citations into it
• Signal Processing — 37 citations into it; #669 of 7,572 by trust here, #414 by citations
• Computer Vision and Pattern Recognition — 24 citations into it
• Generative Models — 18 citations into it
• Model Architectures — 14 citations into it; #485 of 2,278 by trust here, #532 by citations

Whom Naihan Li trusts overall

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

1. Heiga Zen
2. Alex Graves
3. Diederik P. Kingma
4. Yoshua Bengio
5. Geoffrey E. Hinton
6. Aäron van den Oord
7. Keiichi Tokuda
8. Ashish Vaswani
9. Ilya Sutskever
10. Karen Simonyan

Who trusts Naihan Li overall

1. Xu Tan
2. Lei He
3. Yi Ren
4. Yanqing Liu
5. Zhen Zeng
6. Tao Qin
7. Vadim Popov
8. D. Lim
9. Frank K. Soong
10. Jaehyeon Kim

Papers from Naihan Li's perspective

Ordered by how strongly the researchers Naihan Li 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
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

• Neural Speech Synthesis with Transformer Network (2018) — 894 citations
• Close to Human Quality TTS with Transformer (2018) — 107 citations
• RobuTrans: A Robust Transformer-Based Text-to-Speech Model (2020) — 44 citations
• MoBoAligner: a Neural Alignment Model for Non-autoregressive TTS with Monotonic Boundary Search (2020) — 7 citations