Chenfeng Miao

Artificial Intelligence · 2020

A researcher in the Science corpus: 2 papers; trusts 8 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 37 citations into it; #25,215 of 35,345 by trust here, #14,718 by citations
• Signal Processing — 24 citations into it; #5,380 of 7,572 by trust here, #2,276 by citations
• Speech & Audio — 23 citations into it
• Generative Models — 15 citations into it
• Computer Vision and Pattern Recognition — 9 citations into it

Whom Chenfeng Miao trusts overall

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

1. Diederik P. Kingma
2. Wei Ping
3. Yoshua Bengio
4. Kainan Peng
5. Geoffrey E. Hinton
6. Ashish Vaswani
7. Alan W. Black
8. Dzmitry Bahdanau
9. Alex Graves
10. Alex Krizhevsky

Who trusts Chenfeng Miao overall

1. Xu Tan
2. Isaac Elias
3. Yi Ren
4. Songxiang Liu
5. Tao Qin
6. Heiga Zen
7. D. Lim
8. Chenpeng Du
9. Ron J. Weiss
10. Myeonghun Jeong

Papers from Chenfeng Miao's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. ImageNet classification with deep convolutional neural networks
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
7. Gradient-based learning applied to document recognition
8. ImageNet: A large-scale hierarchical image database
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• Flow-TTS: A Non-Autoregressive Network for Text to Speech Based on Flow (2020) — 117 citations
• EfficientTTS: An Efficient and High-Quality Text-to-Speech Architecture (2020) — 46 citations