Shaoshi Ling

Artificial Intelligence · 2018–2020

A researcher in the Science corpus: 3 papers; trusts 100 researchers and is trusted by 14 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 56 citations into it; #9,829 of 35,345 by trust here, #12,387 by citations
• Natural Language Processing — 34 citations into it; #3,238 of 5,688 by trust here, #4,250 by citations
• Signal Processing — 29 citations into it; #1,745 of 7,572 by trust here, #1,820 by citations
• Speech & Audio — 26 citations into it; #568 of 1,076 by trust here, #642 by citations
• Representation & Self-Supervised Learning — 21 citations into it; #1,860 of 3,145 by trust here, #2,042 by citations
• Model Architectures — 8 citations into it
• Computer Vision and Pattern Recognition — 6 citations into it
• Large Language Models — 5 citations into it

Whom Shaoshi Ling trusts overall

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

1. Geoffrey E. Hinton
2. Yu-An Chung
3. Yoshua Bengio
4. Alex Graves
5. Alexei Baevski
6. Oriol Vinyals
7. Diederik P. Kingma
8. Ilya Sutskever
9. Alex Krizhevsky
10. Yann LeCun

Who trusts Shaoshi Ling overall

1. Jasha Droppo
2. Yu-An Chung
3. Puyuan Peng
4. Oguz H. Elibol
5. Abdel-rahman Mohamed
6. Alexander H. Liu
7. Wei-Ning Hsu
8. Andy T. Liu
9. Hung-yi Lee
10. David F. Harwath

Papers from Shaoshi Ling's perspective

Ordered by how strongly the researchers Shaoshi Ling 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. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• Deep Contextualized Acoustic Representations for Semi-Supervised Speech Recognition (2019) — 150 citations
• DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantization (2020) — 117 citations
• CogCompNLP: Your Swiss Army Knife for NLP (2018) — 29 citations