Maud Texier

2021–2022

A researcher in the Science corpus: 2 papers; trusts 0 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Papers from Maud Texier's perspective

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

1. Distilling the Knowledge in a Neural Network (arXiv.org)
2. Language Models are Few-Shot Learners (Neural Information Processing Systems)
3. Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (Journal of machine learning research)
4. Energy and Policy Considerations for Deep Learning in NLP
5. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Journal of machine learning research)
6. Carbon Emissions and Large Neural Network Training
7. Green AI
8. The Evolved Transformer
9. Primer: Searching for Efficient Transformers for Language Modeling
10. GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

Their papers

• Carbon Emissions and Large Neural Network Training (2021) — 1,164 citations
• The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink (2022) — 465 citations