Yun Tang

Artificial Intelligence · 2020–2021

A researcher in the Science corpus: 7 papers; trusts 120 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 190 citations into it; #9,596 of 35,345 by trust here, #8,077 by citations
• Natural Language Processing — 85 citations into it; #4,459 of 5,688 by trust here, #4,032 by citations
• Speech & Audio — 83 citations into it; #863 of 1,076 by trust here, #445 by citations
• Machine Translation — 56 citations into it; #203 of 292 by trust here, #40 by citations
• Computer Vision and Pattern Recognition — 53 citations into it
• Signal Processing — 35 citations into it
• Evaluation & Benchmarks — 19 citations into it
• Large Language Models — 15 citations into it

Whom Yun Tang trusts overall

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

1. Diederik P. Kingma
2. Mattia Antonino Di Gangi
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Alex Graves
6. Changhan Wang
7. Matt Post
8. Ilya Sutskever
9. Alex Krizhevsky
10. Jimmy Ba

Who trusts Yun Tang overall

1. Changhan Wang
2. Xian Li
3. Rong Ye
4. Juan Pino
5. Ann Lee
6. Mingxuan Wang
7. Dong Zhang
8. Paul-Ambroise Duquenne
9. Junyi Ao
10. Hongyu Gong

Papers from Yun Tang's perspective

Ordered by how strongly the researchers Yun Tang 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. Attention is All you Need (Neural Information Processing Systems)
4. ImageNet classification with deep convolutional neural networks
5. Long Short-Term Memory
6. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
7. Gradient-based learning applied to document recognition
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. ImageNet: A large-scale hierarchical image database

Their papers

• Fairseq S2T: Fast Speech-to-Text Modeling with Fairseq (2020) — 334 citations
• Direct Speech-to-Speech Translation With Discrete Units (2021) — 241 citations
• Multilingual Speech Translation from Efficient Finetuning of Pretrained Models (2021) — 167 citations
• A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks (2020) — 82 citations
• Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task (2021) — 96 citations
• Self-Training for End-to-End Speech Translation (2020) — 69 citations
• Direct simultaneous speech to speech translation (2021) — 9 citations