Jonathan Herzig

Artificial Intelligence · 2019–2025

A researcher in the Science corpus: 9 papers; trusts 467 researchers and is trusted by 81 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 292 citations into it; #3,329 of 35,345 by trust here, #2,989 by citations
• Natural Language Processing — 227 citations into it; #1,006 of 5,688 by trust here, #730 by citations
• Computer Vision and Pattern Recognition — 109 citations into it; #4,114 of 21,575 by trust here, #2,478 by citations
• Evaluation & Benchmarks — 73 citations into it; #2,773 of 3,147 by trust here, #1,764 by citations
• Large Language Models — 67 citations into it
• Prompting & Reasoning — 35 citations into it; #343 of 359 by trust here, #225 by citations
• Information Systems — 28 citations into it
• Information Retrieval & Search — 17 citations into it

Whom Jonathan Herzig trusts overall

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

1. Geoffrey E. Hinton
2. Yoshua Bengio
3. Diederik P. Kingma
4. Ilya Sutskever
5. Alex Krizhevsky
6. Jacob Devlin
7. Richard Socher
8. Luke S. Zettlemoyer
9. Kaiming He
10. Yann LeCun

Who trusts Jonathan Herzig overall

1. Wenhu Chen
2. Alon Talmor
3. Bill Yuchen Lin
4. Jonathan Berant
5. Peter Shaw
6. Michihiro Yasunaga
7. Adyasha Maharana
8. Maxwell Nye
9. Linlu Qiu
10. Qian Liu

Papers from Jonathan Herzig's perspective

Ordered by how strongly the researchers Jonathan Herzig 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. ImageNet: A large-scale hierarchical image database
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. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)

Their papers

• CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge (2019) — 2,559 citations
• Learning To Retrieve Prompts for In-Context Learning (2021) — 945 citations
• Unlocking Compositional Generalization in Pre-trained Models Using Intermediate Representations (2021) — 72 citations
• TRUE: Re-evaluating Factual Consistency Evaluation (2022) — 372 citations
• Evaluating the Impact of Model Scale for Compositional Generalization in Semantic Parsing (2022) — 60 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• A Comprehensive Evaluation of Tool-Assisted Generation Strategies (2023) — 11 citations
• Span-based Semantic Parsing for Compositional Generalization (2020) — 105 citations
• TaPas: Weakly Supervised Table Parsing via Pre-training (2020) — 890 citations