Jun Shern Chan

Artificial Intelligence · 2022–2024

A researcher in the Science corpus: 4 papers; trusts 85 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 133 citations into it; #33,673 of 35,345 by trust here, #25,040 by citations
• Natural Language Processing — 83 citations into it; #5,227 of 5,688 by trust here, #3,897 by citations
• Large Language Models — 50 citations into it; #714 of 799 by trust here, #715 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Reinforcement Learning — 34 citations into it
• Alignment & RLHF — 15 citations into it; #37 of 62 by trust here, #43 by citations
• Evaluation & Benchmarks — 15 citations into it
• Safety, Robustness & Interpretability — 13 citations into it

Whom Jun Shern Chan trusts overall

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

1. Alec Radford
2. Geoffrey E. Hinton
3. Yoshua Bengio
4. Diederik P. Kingma
5. Ilya Sutskever
6. Alex Krizhevsky
7. Peter Hase
8. Jeff Wu
9. Richard Socher
10. Kaiming He

Who trusts Jun Shern Chan overall

1. Angelica Chen

Papers from Jun Shern Chan's perspective

Ordered by how strongly the researchers Jun Shern Chan 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. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
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

• Training Language Models with Language Feedback at Scale (2023) — 138 citations
• Learning from Natural Language Feedback (2024) — 29 citations
• Training Language Models with Natural Language Feedback (2022)
• Training Language Models with Language Feedback (2022) — 58 citations