Linghui Chen

Artificial Intelligence · 2014–2020

A researcher in the Science corpus: 6 papers; trusts 60 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 83 citations into it; #10,931 of 35,345 by trust here, #11,750 by citations
• Signal Processing — 52 citations into it; #2,737 of 7,572 by trust here, #2,400 by citations
• Speech & Audio — 34 citations into it
• Natural Language Processing — 22 citations into it
• Computer Vision and Pattern Recognition — 21 citations into it; #21,556 of 21,575 by trust here, #19,766 by citations
• Generative Models — 13 citations into it
• Model Architectures — 12 citations into it
• Probabilistic & Bayesian Methods — 12 citations into it

Whom Linghui Chen trusts overall

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

1. Geoffrey E. Hinton
2. Paul Smolensky
3. Keiichi Tokuda
4. Ruslan Salakhutdinov
5. Heiga Zen
6. Bart Kosko
7. Hideki Kawahara
8. Yoshua Bengio
9. Tomoki Toda
10. Zhenhua Ling

Who trusts Linghui Chen overall

1. Takuhiro Kaneko
2. Hirokazu Kameoka
3. Hsin-Te Hwang
4. Yi-Chiao Wu
5. Junichi Yamagishi
6. Merlijn Blaauw
7. Shinji Takaki
8. Chin-Cheng Hsu
9. Berrak Sisman
10. Yang Gao

Papers from Linghui Chen's perspective

Ordered by how strongly the researchers Linghui Chen 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. GENERATIVE ADVERSARIAL NETS

Their papers

• FeatherTTS: Robust and Efficient attention based Neural TTS (2020) — 5 citations
• FeatherWave: An efficient high-fidelity neural vocoder with multi-band linear prediction (2020) — 22 citations
• The Voice Conversion Challenge 2016 (2016) — 193 citations
• A Deep Generative Architecture for Postfiltering in Statistical Parametric Speech Synthesis (2015) — 50 citations
• DNN-based stochastic postfilter for HMM-based speech synthesis (2014) — 41 citations
• Voice Conversion Using Deep Neural Networks With Layer-Wise Generative Training (2014) — 235 citations