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Eric J. Michaud

Eric J. Michaud has 3 papers in our library, in 2022–2023, most of them on Efficient models & compression; often with Max Tegmark, Ziming Liu, Niklas Nolte.

3 papers in our library · 2022–2023

OpenAlex · ORCID · Wikidata · h-index 7 · 22 works · 232 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 79 times in our library · ⭐ Paper Stars ranking: #7887

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

🎓New York UniversityNew York · United States2026 · 1 papercurrent🎓Massachusetts Institute of TechnologyCambridge · United States2022–2025 · 12 papers🔬The NSF AI Institute for Artificial Intelligence and Fundamental InteractionsCambridge · United States2022–2025 · 6 papers🎓University of California, BerkeleyBerkeley · United States2020–2021 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksComputer Science · 3 worksArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Efficient models & compression · 1 work

💡 Concepts 2Key ideas and methods explained on our concept pages.Scaling laws · 1 workZipf's law · 1 work

 Works by year

2020s

20231 paper20222 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Towards Understanding Grokking: An Effective Theory of Representation Learning (2022 · 65 citations)
    Grokking is the behavior where a neural network fits its training data early but only begins to generalize much later.
  2. The Quantization Model of Neural Scaling (2023 · 30 citations)
    The paper tackles a puzzle about scaling. Average loss falls smoothly as a power law when models and datasets grow, yet specific abilities often seem to switch on abruptly. To reconcile the two, the authors propose the Quantization Hypothesis. It holds that prediction tasks rely on a countable set of discrete skills…
  3. Omnigrok: Grokking Beyond Algorithmic Data (2022 · 29 citations)
    Grokking is when a network fits its training data early but only begins to generalize much later. It had been documented mainly on algorithmic tasks, and its cause was unclear. Liu, Michaud and Tegmark study it through loss-landscape geometry. They use a "reduced" loss, obtained by optimizing over weight directions…

 Co-authors here

3 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 79 times (of the 149,439 papers whose bibliographies we read); 25 citing papers with a page here →.

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