Samuel Gehman

Artificial Intelligence · 2020–2025

A researcher in the Science corpus: 2 papers; trusts 22 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 61 citations into it; #11,401 of 35,345 by trust here, #4,137 by citations
• Natural Language Processing — 48 citations into it
• Safety, Robustness & Interpretability — 31 citations into it; #932 of 2,251 by trust here, #311 by citations
• Large Language Models — 14 citations into it; #280 of 799 by trust here, #325 by citations
• Communication — 9 citations into it
• Evaluation & Benchmarks — 7 citations into it

Whom Samuel Gehman trusts overall

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

1. Alec Radford
2. Yann LeCun
3. Yoshua Bengio
4. Geoffrey E. Hinton
5. Diederik P. Kingma
6. Ilya Sutskever
7. Alex Krizhevsky
8. Kaiming He
9. Zeerak Talat
10. Elizabeth B.-N. Sanders

Who trusts Samuel Gehman overall

1. Sachin Kumar
2. Patrick Schramowski
3. Eric Michael Smith
4. Alisa Liu
5. Emily Sheng
6. Yu Meng
7. Emily Dinan
8. Ximing Lu
9. Xiangyu Qi
10. Maarten Sap

Papers from Samuel Gehman's perspective

Ordered by how strongly the researchers Samuel Gehman 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. 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. ImageNet: A large-scale hierarchical image database
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

• RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models (2020) — 1,775 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations