Clark W. Barrett

Artificial Intelligence · with Chen · 2023

A researcher in the Science corpus: 2 papers; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 85 citations into it
• Efficiency & Compression — 44 citations into it
• Large Language Models — 36 citations into it

Whom Clark W. Barrett trusts

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

1. Dan Alistarh (in Efficiency & Compression)
2. Elias Frantar (in Efficiency & Compression)
3. Noam Shazeer (in Artificial Intelligence)
4. Ilya Sutskever (in Artificial Intelligence)

Papers from Clark W. Barrett's perspective

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

1. Attention is All you Need (Neural Information Processing Systems)
2. Language Models are Few-Shot Learners (Neural Information Processing Systems)
3. RoBERTa: A Robustly Optimized BERT Pretraining Approach (arXiv.org)
4. Decoupled Weight Decay Regularization
5. Pointer Sentinel Mixture Models
6. XLNet: Generalized Autoregressive Pretraining for Language Understanding (Neural Information Processing Systems)
7. PyTorch: An Imperative Style, High-Performance Deep Learning Library
8. Generating Long Sequences with Sparse Transformers
9. Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity (Journal of machine learning research)
10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers

• High-throughput Generative Inference of Large Language Models with a Single GPU (2023) — 797 citations
• H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models (2023) — 817 citations