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Albert Gu

Albert Gu has 2 papers in our library, in 2023–2024, most of them on Language models & pretraining; often with Antonio Orvieto, Brandon Norick, Caglar Gulcehre.

2 papers in our library · 2023–2024

OpenAlex · h-index 20 · 57 works · 2,951 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 1,191 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #6636

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

🎓Carnegie Mellon UniversityPittsburgh · United States2013–2026 · 16 paperscurrent🎓Stanford UniversityStanford · United States2018–2023 · 23 papers🎓University at Buffalo, State University of New YorkBuffalo · United States2018–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 2 worksLanguage and Linguistics · 2 works

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

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Resurrecting Recurrent Neural Networks for Long Sequences (2023 · 118 citations)
    Classic RNNs are cheap at inference on long inputs, but they train slowly and suffer from vanishing or exploding gradients.
  2. An Empirical Study of Mamba-based Language Models (2024 · 67 citations)
    Selective state-space models (SSMs) such as Mamba could make inference cheaper than in Transformers. They avoid attention's quadratic cost and the ever-growing key-value cache. However, earlier comparisons with identical training data covered only small models. This NVIDIA-led report addresses that gap. It trains…

All works

7 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. Mamba: Linear-Time Sequence Modeling with Selective State Spaces ↗arXiv:2312.00752 · no summary yetAlbert Gu, Tri Dao2023 · 1,726 citations
  2. Efficiently Modeling Long Sequences with Structured State Spaces ↗arXiv:2111.00396 · no summary yetAlbert Gu, Karan Goel, Christopher Ré2021 · 791 citations
  3. HiPPO: Recurrent Memory with Optimal Polynomial Projections ↗arXiv:2008.07669 · no summary yetAlbert Gu et al.2020 · 205 citations
  4. Resurrecting Recurrent Neural Networks for Long SequencesarXiv:2303.06349Antonio Orvieto, Samuel L Smith, Albert Gu, et al.2023 · 118 citations
  5. An Empirical Study of Mamba-based Language ModelsarXiv:2406.07887Roger Waleffe, Wonmin Byeon, Duncan Riach, et al.2024 · 67 citations
  6. Transformers to SSMs: Distilling Quadratic Knowledge to Subquadratic Models ↗arXiv:2408.10189 · no summary yetAviv Bick et al.2024 · 29 citations
  7. Joint Distillation for Fast Likelihood Evaluation and Sampling in Flow-based Models ↗arXiv (Cornell University) · no summary yetAi, Xinyue et al.2025 · 1 citation

 Co-authors here

9 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 1,191 times (of the 149,439 papers whose bibliographies we read); 117 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/albert-gu/