Simeng Sun

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 4 papers; trusts 114 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 118 citations into it; #31,614 of 35,345 by trust here, #17,776 by citations
• Natural Language Processing — 65 citations into it
• Computer Vision and Pattern Recognition — 50 citations into it
• Model Architectures — 43 citations into it
• Large Language Models — 29 citations into it
• Evaluation & Benchmarks — 11 citations into it

Whom Simeng Sun trusts overall

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

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Ashish Vaswani
5. Ilya Sutskever
6. Alex Krizhevsky
7. Kyunghyun Cho
8. Yann LeCun
9. Alex Graves
10. Kaiming He

Who trusts Simeng Sun overall

1. Kalpesh Krishna
2. Sebastian Gehrmann
3. Aurko Roy
4. Yuhuai Wu
5. Andrew Hoang
6. Antoine Bosselut
7. Mohit Iyyer

Papers from Simeng Sun's perspective

Ordered by how strongly the researchers Simeng Sun 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. Attention is All you Need (Neural Information Processing Systems)
3. ImageNet classification with deep convolutional neural networks
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. Gradient-based learning applied to document recognition
8. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
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

• Do Long-Range Language Models Actually Use Long-Range Context? (2021) — 107 citations
• How to Compare Summarizers without Target Length? Pitfalls, Solutions and Re-Examination of the Neural Summarization Literature (2019) — 53 citations
• Energy-Based Reranking: Improving Neural Machine Translation Using Energy-Based Models (2020) — 67 citations
• RULER: What's the Real Context Size of Your Long-Context Language Models? (2024) — 1,085 citations