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Daniel Y. Fu

Daniel Y. Fu has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining, GPU & parallel computing; often with Anton Alexandrov, Beidi Chen, Ben Athiwaratkun.

2 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 7 · 35 works · 371 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2,183 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #3248

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of California San DiegoSan Diego · United States2025–2026 · 4 paperscurrent🤝TogetherLacey · United States2026 · 1 papercurrent🎓Stanford UniversityStanford · United States2019–2024 · 14 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksHardware and Architecture · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksGPU & parallel computing · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU (2023 · 402 citations)
    Running very large language models usually requires several expensive accelerators. GPT-175B alone needs about 325GB just for its weights. Many workloads, however, are batch jobs where latency matters little, such as benchmarking, information extraction and data wrangling. The authors build FlexGen, an inference…
  2. RedPajama: an Open Dataset for Training Large Language Models (2024 · 260 citations)
    Many leading LLMs are trained on data whose sources and cleaning steps are never disclosed. This makes open research on pretraining corpora slow and hard to replicate. The authors respond with two public releases. RedPajama-V1 rebuilds the LLaMA-1 training mixture from its brief published description and records…

All works

6 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness ↗arXiv:2205.14135 · no summary yetTri Dao et al.2022 · 2,495 citations
  2. FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPUarXiv:2303.06865Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.2023 · 402 citations
  3. RedPajama: an Open Dataset for Training Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Maurice Weber, Daniel Fu, Quentin Anthony, et al.2024 · 260 citations
  4. Hyena Hierarchy: Towards Larger Convolutional Language Models ↗arXiv:2302.10866 · no summary yetMichael Poli et al.2023 · 182 citations
  5. Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions ↗neural information processing systems · no summary yetStefano Massaroli et al.2023 · 13 citations
  6. Exploring Diffusion Transformer Designs via Grafting ↗neural information processing systems · no summary yetKeshigeyan Chandrasegaran et al.2025 · 5 citations

 Co-authors here

12 of 17 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 2,183 times (of the 149,439 papers whose bibliographies we read); 227 citing papers with a page here →.

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