Yilun Zhao

Artificial Intelligence · 2022–2023

A researcher in the Science corpus: 4 papers; trusts 16 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 161 citations into it
• Natural Language Processing — 81 citations into it
• Evaluation & Benchmarks — 70 citations into it

Whom Yilun Zhao trusts overall

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

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

Papers from Yilun Zhao's perspective

Ordered by how strongly the researchers Yilun Zhao 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. Long Short-Term Memory
5. ImageNet classification with deep convolutional neural networks
6. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
7. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
8. Gradient-based learning applied to document recognition
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

• Revisiting the Gold Standard: Grounding Summarization Evaluation with Robust Human Evaluation (2022) — 179 citations
• MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning (2023) — 457 citations
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models (2023) — 44 citations
• Investigating Data Contamination in Modern Benchmarks for Large Language Models (2023)