James Lee-Thorp

Artificial Intelligence · 2021–2023

A researcher in the Science corpus: 5 papers; trusts 222 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 148 citations into it; #33,819 of 35,345 by trust here, #9,760 by citations
• Computer Vision and Pattern Recognition — 89 citations into it
• Model Architectures — 62 citations into it; #2,121 of 2,278 by trust here, #621 by citations
• Efficiency & Compression — 58 citations into it; #860 of 1,264 by trust here, #1,174 by citations
• Large Language Models — 55 citations into it; #481 of 799 by trust here, #797 by citations
• Natural Language Processing — 42 citations into it; #5,451 of 5,688 by trust here, #2,220 by citations
• Evaluation & Benchmarks — 12 citations into it
• Hardware and Architecture — 9 citations into it

Whom James Lee-Thorp trusts overall

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

1. Geoffrey E. Hinton
2. Noam Shazeer
3. Yoshua Bengio
4. Diederik P. Kingma
5. Alex Krizhevsky
6. Yi Tay
7. Ilya Sutskever
8. Yann LeCun
9. Kaiming He
10. Ashish Vaswani

Who trusts James Lee-Thorp overall

1. William Fedus
2. Yi Tay
3. Alasdair Tran
4. Zhengzhong Tu
5. Yongming Rao
6. J. Ainslie
7. Mostafa Dehghani
8. John Guibas
9. Antonio Orvieto
10. Joseph Dean

Papers from James Lee-Thorp's perspective

Ordered by how strongly the researchers James Lee-Thorp 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. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. ImageNet: A large-scale hierarchical image database
7. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (North American Chapter of the Association for Computational Linguistics)
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

• GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints (2023) — 1,604 citations
• FNet: Mixing Tokens with Fourier Transforms (2021) — 751 citations
• Scaling Up Models and Data with t5x and seqio (2022) — 224 citations
• Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints (2022) — 215 citations
• Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT (2022) — 15 citations