Sanyuan Chen

Artificial Intelligence · 2020–2024

A researcher in the Science corpus: 13 papers; trusts 214 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 438 citations into it; #16,325 of 35,345 by trust here, #4,231 by citations
• Signal Processing — 296 citations into it; #4,391 of 7,572 by trust here, #1,094 by citations
• Speech & Audio — 244 citations into it
• Computer Vision and Pattern Recognition — 107 citations into it
• Natural Language Processing — 90 citations into it
• Representation & Self-Supervised Learning — 77 citations into it
• Generative Models — 54 citations into it
• Model Architectures — 50 citations into it

Whom Sanyuan Chen trusts overall

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

1. Geoffrey E. Hinton
2. Diederik P. Kingma
3. Yoshua Bengio
4. Alex Krizhevsky
5. Alex Graves
6. Kaiming He
7. Ilya Sutskever
8. Oriol Vinyals
9. Yann LeCun
10. Jimmy Ba

Who trusts Sanyuan Chen overall

1. Zhuo Chen
2. Jian Wu
3. Yu Wu
4. Shujie Liu
5. Chengyi Wang
6. Zi-Hua Zhang
7. Joel Jang
8. Ziyue Jiang
9. Jinyu Li
10. Takuya Yoshioka

Papers from Sanyuan Chen's perspective

Ordered by how strongly the researchers Sanyuan 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. Gradient-based learning applied to document recognition
8. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
9. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
10. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)

Their papers

• WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing (2021) — 3,371 citations
• Unispeech-Sat: Universal Speech Representation Learning With Speaker Aware Pre-Training (2021) — 138 citations
• Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting (2020) — 270 citations
• Speak Foreign Languages with Your Own Voice: Cross-Lingual Neural Codec Language Modeling (2023) — 275 citations
• Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers (2023) — 1,289 citations
• Continuous Speech Separation with Conformer (2020) — 151 citations
• VALL-E 2: Neural Codec Language Models are Human Parity Zero-Shot Text to Speech Synthesizers (2024) — 206 citations
• SpeechLM: Enhanced Speech Pre-Training With Unpaired Textual Data (2022) — 78 citations
• Don’t Shoot Butterfly with Rifles: Multi-Channel Continuous Speech Separation with Early Exit Transformer (2020) — 35 citations
• SpeechX: Neural Codec Language Model as a Versatile Speech Transformer (2023) — 123 citations
• BEATs: Audio Pre-Training with Acoustic Tokenizers (2022) — 641 citations
• Ultra Fast Speech Separation Model with Teacher Student Learning (2021) — 15 citations
• Investigation of Practical Aspects of Single Channel Speech Separation for ASR (2021) — 21 citations