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David Saxton

David Saxton has 2 papers in our library, in 2018–2019, most of them on Reasoning; often with Edward Grefenstette, Pushmeet Kohli, Felix Hill.

2 papers in our library · 2018–2019

OpenAlex · ORCID · h-index 12 · 24 works · 1,567 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 323 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5461

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

🏢Google DeepMindLondon · United Kingdom2016–2024 · 3 paperscurrent🎓University of CambridgeCambridge · United Kingdom2010–2016 · 7 papers🔬Instituto Nacional de Matemática Pura e AplicadaRio de Janeiro · Brazil2013–2016 · 5 papers🏢GoogleMountain View · United States2018–2019 · 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksComputational Theory and Mathematics · 1 workMathematics · 1 work

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

 Works by year

2010s

20191 paper20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Analysing Mathematical Reasoning Abilities of Neural Models (2019 · 288 citations)
    Neural sequence models handle pattern-matching tasks well but often fail at the rule-based, compositional reasoning that school mathematics requires.
  2. Can Neural Networks Understand Logical Entailment? (2018 · 44 citations)
    The paper asks whether neural networks can decide if one propositional formula logically entails another. The task works as a clean probe of how well architectures capture purely structural properties of sequences, because variable names carry no meaning and only the connectives matter. The DeepMind authors build a…

All works

4 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. Unifying Count-Based Exploration and Intrinsic Motivation ↗arXiv:1606.01868 · no summary yetMarc G. Bellemare et al.2016 · 311 citations
  2. Analysing Mathematical Reasoning Abilities of Neural ModelsarXiv:1904.01557David Saxton, Edward Grefenstette, Felix Hill, et al.2019 · 288 citations
  3. Unsupervised Predictive Memory in a Goal-Directed Agent ↗arXiv (Cornell University) · no summary yetGreg Wayne et al.2018 · 140 citations
  4. Can Neural Networks Understand Logical Entailment?arXiv:1802.08535Richard Evans, David Saxton, David Amos, et al.2018 · 44 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 323 times (of the 149,439 papers whose bibliographies we read); 106 citing papers with a page here →.

Updated

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