Chao Weng

Artificial Intelligence · 2014–2023

A researcher in the Science corpus: 17 papers; trusts 310 researchers and is trusted by 13 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 336 citations into it; #11,563 of 35,345 by trust here, #5,028 by citations
• Signal Processing — 232 citations into it; #2,176 of 7,572 by trust here, #557 by citations
• Speech & Audio — 188 citations into it; #650 of 1,076 by trust here, #317 by citations
• Natural Language Processing — 132 citations into it
• Computer Vision and Pattern Recognition — 108 citations into it
• Generative Models — 83 citations into it
• Model Architectures — 54 citations into it; #2,043 of 2,278 by trust here, #1,882 by citations
• Representation & Self-Supervised Learning — 24 citations into it

Whom Chao Weng trusts overall

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

1. Geoffrey E. Hinton
2. Alex Graves
3. Dong Yu
4. Yoshua Bengio
5. Diederik P. Kingma
6. Daniel Povey
7. Abdel-rahman Mohamed
8. George E. Dahl
9. Oriol Vinyals
10. Alex Krizhevsky

Who trusts Chao Weng overall

1. Dong Yu
2. Jinyu Li
3. Peidong Wang
4. Arun Narayanan
5. Shinji Watanabe
6. Jia Cui
7. Felix Weninger
8. Rohit Prabhavalkar
9. William Chan
10. Herman Kamper

Papers from Chao Weng's perspective

Ordered by how strongly the researchers Chao Weng 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)

Their papers

• Diffsound: Discrete Diffusion Model for Text-to-Sound Generation (2022) — 434 citations
• GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10, 000 Hours of Transcribed Audio (2021) — 607 citations
• Recurrent deep neural networks for robust speech recognition (2014) — 139 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
• Token-wise Training for Attention Based End-to-end Speech Recognition (2019) — 5 citations
• DurIAN: Duration Informed Attention Network For Multimodal Synthesis (2019) — 119 citations
• HiFi-Codec: Group-residual Vector quantization for High Fidelity Audio Codec (2023) — 231 citations
• Make-A-Voice: Unified Voice Synthesis With Discrete Representation (2023) — 38 citations
• InstructTTS: Modelling Expressive TTS in Discrete Latent Space With Natural Language Style Prompt (2023) — 174 citations
• Feature space maximum a posteriori linear regression for adaptation of deep neural networks (2014) — 21 citations
• VARA-TTS: Non-Autoregressive Text-to-Speech Synthesis based on Very Deep VAE with Residual Attention (2021) — 24 citations
• DurIAN: Duration Informed Attention Network for Speech Synthesis (2020) — 80 citations
• Large Margin Training for Attention Based End-to-End Speech Recognition (2019) — 8 citations
• Deep Neural Networks for Single-Channel Multi-Talker Speech Recognition (2015) — 101 citations
• Single-channel mixed speech recognition using deep neural networks (2014) — 36 citations