Teven Le Scao

Artificial Intelligence · 2019–2024

A researcher in the Science corpus: 15 papers; trusts 133 researchers and is trusted by 38 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 592 citations into it; #5,833 of 35,345 by trust here, #789 by citations
• Natural Language Processing — 402 citations into it
• Large Language Models — 241 citations into it; #164 of 799 by trust here, #103 by citations
• Evaluation & Benchmarks — 224 citations into it
• Computer Vision and Pattern Recognition — 185 citations into it
• Model Architectures — 58 citations into it
• Prompting & Reasoning — 48 citations into it; #50 of 359 by trust here, #67 by citations
• Optimization & Training — 29 citations into it

Whom Teven Le Scao trusts overall

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

1. Alec Radford
2. Diederik P. Kingma
3. Geoffrey E. Hinton
4. Yoshua Bengio
5. Ilya Sutskever
6. Noam Shazeer
7. Alex Krizhevsky
8. Jimmy Ba
9. Timo Schick
10. Kaiming He

Who trusts Teven Le Scao overall

1. Swaroop Mishra
2. Yu Meng
3. Eyal Ben-David
4. Daniel Khashabi
5. Aman Madaan
6. Ravsehaj Singh Puri
7. Robert L Logan IV
8. Bonan Min
9. Bosheng Ding
10. Chitta Baral

Papers from Teven Le Scao's perspective

Ordered by how strongly the researchers Teven Le Scao 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. ImageNet: A large-scale hierarchical image database
8. Gradient-based learning applied to document recognition
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

• Multitask Prompted Training Enables Zero-Shot Task Generalization (2021) — 2,030 citations
• HuggingFace's Transformers: State-of-the-art Natural Language Processing (2019) — 4,184 citations
• What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? (2022) — 236 citations
• How many data points is a prompt worth? (2021) — 323 citations
• Mistral 7B (2023) — 3,685 citations
• Crosslingual Generalization through Multitask Finetuning (2023) — 602 citations
• What Language Model to Train if You Have One Million GPU Hours? (2022) — 125 citations
• Scaling Data-Constrained Language Models (2023) — 422 citations
• The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset (2023) — 217 citations
• Datasets: A Community Library for Natural Language Processing (2021) — 775 citations
• Mixtral of Experts (2024) — 2,015 citations
• BLOOM: A 176B-Parameter Open-Access Multilingual Language Model (2022) — 2,975 citations
• Multitask Prompted Training Enables Zero-Shot Task Generalization (2022) — 242 citations
• Transformers: State-of-the-Art Natural Language Processing (2020) — 24 citations
• HuggingFace's Transformers: State-of-the-art Natural Language Processing (2019)