--- title: "Eric J. Michaud — author profile and papers — Elamur Research" description: "Eric J. Michaud, New York University: 3 papers on Elamur Research (2022–2023), h-index 7 — affiliations, topics, co-authors and who cites…" canonical: "https://research.elamur.ai/free/en/authors/eric-j-michaud/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/eric-j-michaud/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / Eric J. Michaud # Eric J. Michaud Eric J. Michaud has 3 papers in our library, in 2022–2023, most of them on [Efficient models & compression](https://md.research.elamur.ai/free/en/topics/efficient-models/); often with [Max Tegmark](https://md.research.elamur.ai/free/en/authors/max-tegmark/), [Ziming Liu](https://md.research.elamur.ai/free/en/authors/ziming-liu/), [Niklas Nolte](https://md.research.elamur.ai/free/en/authors/niklas-nolte/). 3 papers in our library · 2022–2023 [OpenAlex](https://openalex.org/A5071048251) · [ORCID](https://orcid.org/0000-0001-7912-1953) · [Wikidata](https://www.wikidata.org/wiki/Q104560530) · [**h-index 7**](https://openalex.org/authors/A5071048251) · [22 works](https://openalex.org/works?filter=author.id:A5071048251) · [232 citations](https://openalex.org/works?filter=author.id:A5071048251&sort=cited_by_count:desc) OpenAlex, 2026-10-14 [📄 3 works in our library](https://md.research.elamur.ai/free/en/authors/eric-j-michaud/#papers) · [🔗 Cited 79 times in our library](https://md.research.elamur.ai/free/en/authors/eric-j-michaud/cited-by/) · [⭐ Paper Stars ranking: #7887](https://md.research.elamur.ai/free/en/paper-stars/page/79/#star-7887) The profile is the library's match of the name; two people with one name are not always told apart. Elsewhere: [Google Scholar](https://scholar.google.com/citations?view_op=search_authors&mauthors=Eric+J.+Michaud) · [Semantic Scholar](https://www.semanticscholar.org/search?q=Eric+J.+Michaud) · [dblp](https://dblp.org/search?q=Eric+J.+Michaud) · [ACL Anthology](https://aclanthology.org/search/?q=Eric+J.+Michaud) ## Affiliations [🎓**New York University** New York · United States 2026 · 1 paper*current*](https://md.research.elamur.ai/free/en/universities/new-york-university/) · [🎓**Massachusetts Institute of Technology** Cambridge · United States 2022–2025 · 12 papers](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) · [🔬**The NSF AI Institute for Artificial Intelligence and Fundamental Interactions** Cambridge · United States 2022–2025 · 6 papers](https://md.research.elamur.ai/free/en/labs/the-nsf-ai-institute-for-artificial-intelligence-and-fundamental-interactions/) · [🎓**University of California, Berkeley** Berkeley · United States 2020–2021 · 4 papers](https://md.research.elamur.ai/free/en/universities/university-of-california-berkeley/) 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 4 The field of science this work belongs to (OpenAlex fields).[Artificial Intelligence · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Computer Science · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 1 work](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) 🧭 Topics 1 Research subjects and languages the work is about.[Efficient models & compression · 1 work](https://md.research.elamur.ai/free/en/topics/efficient-models/) 💡 Concepts 2 Key ideas and methods explained on our concept pages.[Scaling laws · 1 work](https://md.research.elamur.ai/free/en/concepts/scaling-laws/) · [Zipf's law · 1 work](https://md.research.elamur.ai/free/en/concepts/zipfs-law/) ## Works by year ### 2020s [**2023** 1 paper](https://md.research.elamur.ai/free/en/authors/eric-j-michaud/year/2023/) · [**2022** 2 papers](https://md.research.elamur.ai/free/en/authors/eric-j-michaud/year/2022/) ## Papers The most cited first (OpenAlex and our library). 1. **[Towards Understanding Grokking: An Effective Theory of Representation Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-towards-understanding-grokking-effective-theory/)** *(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](https://md.research.elamur.ai/free/en/papers/michaud-2023-quantization-model-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](https://md.research.elamur.ai/free/en/papers/liu-2022-omnigrok-grokking-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. - [Max Tegmark](https://md.research.elamur.ai/free/en/authors/max-tegmark/) ×3 - [Ziming Liu](https://md.research.elamur.ai/free/en/authors/ziming-liu/) ×3 - [Niklas Nolte](https://md.research.elamur.ai/free/en/authors/niklas-nolte/) ## Venues Where their works here were published, the most works first, with the editions of those that have a page. - [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/): [NeurIPS 2022](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2022/) ## 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 →](https://md.research.elamur.ai/free/en/authors/eric-j-michaud/cited-by/). Updated 3 November 2026