Mengyao Cao

Artificial Intelligence · 2020–2023

A researcher in the Science corpus: 3 papers; trusts 94 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 69 citations into it; #20,588 of 35,345 by trust here, #16,471 by citations
• Natural Language Processing — 62 citations into it; #3,498 of 5,688 by trust here, #3,421 by citations
• Safety, Robustness & Interpretability — 18 citations into it
• Evaluation & Benchmarks — 16 citations into it
• Large Language Models — 13 citations into it
• Computer Vision and Pattern Recognition — 12 citations into it
• Reinforcement Learning — 5 citations into it

Whom Mengyao Cao trusts overall

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

1. Chin-Yew Lin
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Yoshua Bengio
5. Richard S. Sutton
6. Ilya Sutskever
7. Alex Krizhevsky
8. Yann LeCun
9. Christian Szegedy
10. Kaiming He

Who trusts Mengyao Cao overall

1. Shuyang Cao
2. Tanya Goyal
3. James Thorne
4. Vidhisha Balachandran
5. Sihao Chen
6. Zhiyuan Zeng
7. Chenguang Zhu
8. Yang Liu
9. Greg Durrett
10. Andreas Vlachos

Papers from Mengyao Cao's perspective

Ordered by how strongly the researchers Mengyao Cao trusts endorse each paper. Same corpus, a different order for every researcher.

1. Attention is All you Need (Neural Information Processing Systems)
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. ImageNet classification with deep convolutional neural networks
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. ImageNet: A large-scale hierarchical image database
9. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
10. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• Factual Error Correction for Abstractive Summarization Models (2020) — 189 citations
• Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarization (2021) — 193 citations
• Systematic Rectification of Language Models via Dead-end Analysis (2023) — 22 citations