Thibault Formal

Artificial Intelligence · 2021–2026

A researcher in the Science corpus: 4 papers; trusts 57 researchers and is trusted by 6 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it
• Information Retrieval & Search — 59 citations into it; #595 of 1,022 by trust here, #484 by citations
• Computer Vision and Pattern Recognition — 34 citations into it
• Information Systems — 23 citations into it
• Natural Language Processing — 22 citations into it; #5,105 of 5,688 by trust here, #4,361 by citations
• Large Language Models — 20 citations into it
• Retrieval-Augmented Generation — 13 citations into it
• Representation & Self-Supervised Learning — 6 citations into it

Whom Thibault Formal trusts overall

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

1. Nils Reimers
2. David R. Cheriton
3. Rodrigo Nogueira
4. Zhuyun Dai
5. Tiancheng Zhao
6. Siu Kwan Lam
7. Sebastian Hofstätter
8. Nick Craswell
9. Omar Khattab
10. Geoffrey E. Hinton

Who trusts Thibault Formal overall

1. Niklas Muennighoff
2. Keshav Santhanam
3. Omar Khattab
4. Shengyao Zhuang
5. Gautier Izacard
6. Zhuyun Dai
7. Jon Saad-Falcon
8. C. Lassance
9. Michele Bevilacqua
10. Arvind Neelakantan

Papers from Thibault Formal's perspective

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

1. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
2. Attention is All you Need (Neural Information Processing Systems)
3. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
4. ImageNet classification with deep convolutional neural networks
5. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
6. Long Short-Term Memory
7. ImageNet: A large-scale hierarchical image database
8. Neural Machine Translation by Jointly Learning to Align and Translate (International Conference on Learning Representations)
9. Sequence to Sequence Learning with Neural Networks (Neural Information Processing Systems)
10. Gradient-based learning applied to document recognition

Their papers

• SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking (2021) — 611 citations
• On the Challenges and Opportunities of Learned Sparse Retrieval for Code (2026)
• BERGEN: A Benchmarking Library for Retrieval-Augmented Generation (2024)
• SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval (2021) — 247 citations