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Thomas G. Dietterich

Thomas G. Dietterich has 2 papers in our library, in 2014–2019, most of them on Coreference resolution; often with Dan Hendrycks.

2 papers in our library · 2014–2019

OpenAlex · ORCID · h-index 65 · 285 works · 34,885 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 162 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4397

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

🎓Oregon State UniversityCorvallis · United States1986–2026 · 223 paperscurrent🏛️Advanced Research + Invention AgencyLondon · United Kingdom2024–2025 · 2 paperscurrent🎓Stanford UniversityStanford · United States1980–1985 · 6 papers🎓University of WyomingLaramie · United States2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksAgricultural and Biological Sciences · 1 workArtificial Intelligence · 2 worksComputer Vision and Pattern Recognition · 1 workPlant Science · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

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

 Works by year

2010s

20191 paper20141 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (2019 · 434 citations)
    Most robustness research in computer vision focuses on worst-case adversarial attacks. Deployed classifiers, however, mostly face everyday degradations such as sensor noise, blur, bad weather and compression. Hendrycks and Dietterich formalize two average-case notions of robustness. Corruption robustness is accuracy…
  2. Prune-and-Score: Learning for Greedy Coreference Resolution (2014 · 36 citations)
    Most incremental coreference systems rely on one learned scoring function to make every clustering decision.

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. Benchmarking Neural Network Robustness to Common Corruptions and PerturbationsarXiv:1903.12261Dan Hendrycks, Thomas Dietterich2019 · 434 citations
  2. Learning non-redundant codebooks for classifying complex objects ↗International Conference on Machine Learning · no summary yetWei Zhang et al.2009 · 72 citations
  3. Prune-and-Score: Learning for Greedy Coreference ResolutionConference on Empirical Methods in Natural Language Processing (EMNLP)Chao Ma, Janardhan Rao Doppa, J. Walker Orr, et al.2014 · 36 citations
  4. International Scientific Report on the Safety of Advanced AI (Interim Report) ↗arXiv (Cornell University) · no summary yetYoshua Bengio et al.2024 · 13 citations

 Co-authors here

1 co-author 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 162 times (of the 149,439 papers whose bibliographies we read); 20 citing papers with a page here →.

Updated

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