Shang-Wen Li

Artificial Intelligence · 2017–2024

A researcher in the Science corpus: 12 papers; trusts 432 researchers and is trusted by 12 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 500 citations into it; #13,781 of 35,345 by trust here, #4,656 by citations
• Computer Vision and Pattern Recognition — 237 citations into it
• Signal Processing — 196 citations into it; #4,123 of 7,572 by trust here, #2,343 by citations
• Speech & Audio — 184 citations into it; #657 of 1,076 by trust here, #445 by citations
• Representation & Self-Supervised Learning — 180 citations into it; #2,163 of 3,145 by trust here, #2,330 by citations
• Natural Language Processing — 139 citations into it
• Large Language Models — 75 citations into it
• Evaluation & Benchmarks — 68 citations into it

Whom Shang-Wen Li trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Graves
6. Alex Krizhevsky
7. Alexei Baevski
8. Kaiming He
9. Yann LeCun
10. Tomas Mikolov

Who trusts Shang-Wen Li overall

1. Puyuan Peng
2. Yu-An Chung
3. Lasse Borgholt
4. Dongwei Jiang
5. Sanyuan Chen
6. Stephen Roller
7. Richard He Bai
8. David F. Harwath

Papers from Shang-Wen Li's perspective

Ordered by how strongly the researchers Shang-Wen Li 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. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• DINOv2: Learning Robust Visual Features without Supervision (2023) — 9,372 citations
• Learning Robust Dialog Policies in Noisy Environments (2017) — 24 citations
• Chameleon: Mixed-Modal Early-Fusion Foundation Models (2024) — 918 citations
• TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech (2020) — 414 citations
• SUPERB: Speech processing Universal PERformance Benchmark (2021) — 1,225 citations
• Audio Albert: A Lite Bert for Self-Supervised Learning of Audio Representation (2020) — 166 citations
• Semi-Supervised Spoken Language Understanding via Self-Supervised Speech and Language Model Pretraining (2020) — 65 citations
• SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities (2022) — 133 citations
• The Llama 3 Herd of Models (2024) — 17,085 citations
• SeamlessM4T: Massively Multilingual&Multimodal Machine Translation (2023) — 202 citations
• Self-Supervised Speech Representation Learning: A Review (2022) — 515 citations
• Lifelong Pretraining: Continually Adapting Language Models to Emerging Corpora (2021) — 153 citations