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Alexander A. Alemi

Alexander A. Alemi has 2 papers in our library, in 2016–2024, most of them on Reasoning; often with Christian Szegedy, Francois Chollet, Geoffrey Irving.

2 papers in our library · 2016–2024

OpenAlex · ORCID · Wikidata · h-index 18 · 52 works · 18,249 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 224 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6823

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

🏢GoogleMountain View · United States2015–2023 · 21 papersEmployment 2015Employment since 2016🎓Cornell UniversityIthaca · United States2005–2020 · 9 papersEducation 2005–2013 · MS, PhysicsEducation 2009–2015 · PhD, Physics
🏛️Disney Research BostonCambridge · United States2014–2016Employment 2014 · Research InternEmployment 2015–2016 · Postdoc
🏛️Google IncMountain View · United States2015–2016Employment 2015 · Research Intern, Machine PerceptionEmployment since 2016
🎓California Institute of TechnologyPasadena · United States2005–2009Education 2005–2009 · BS, Physics

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 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workComputational Theory and Mathematics · 1 workLanguage and Linguistics · 1 work

🧭 Topics 1Research subjects and languages the work is about.Reasoning · 1 work

 Works by year

2020s

20241 paper

2010s

20161 paper

 Papers

The most cited first (OpenAlex and our library).

  1. DeepMath - Deep Sequence Models for Premise Selection (2016 · 178 citations)
    Automated theorem provers (ATPs) slow down badly when given a very large library of facts. Choosing a small set of useful premises for a new conjecture is therefore a major bottleneck in formalizing mathematics. The authors treat premise selection as a pairwise scoring task. Two separate neural encoders turn a…
  2. Scaling Exponents Across Parameterizations and Optimizers (2024 · 15 citations)
    Scaling a Transformer from small to large width requires choosing how the initialization, parameter multipliers and learning rates change with width.

All works

3 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. DeepMath - Deep Sequence Models for Premise SelectionarXiv:1606.04442Alex A. Alemi, Francois Chollet, Niklas Een, et al.2016 · 178 citations
  2. Scaling Exponents Across Parameterizations and OptimizersarXiv:2407.05872Katie Everett, Lechao Xiao, Mitchell Wortsman, et al.2024 · 15 citations
  3. Tree-Structured Variational Autoencoder ↗no summary yetRichard Shin et al.2017 · 3 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 224 times (of the 149,439 papers whose bibliographies we read); 102 citing papers with a page here →.

Updated

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