Elamur Research⌕
☰
home / authors / Yuri Burda

Yuri Burda

Yuri Burda has 3 papers in our library, in 2018–2022, most of them on Language models & pretraining, Evaluation & benchmarks, Efficient models & compression; often with Harri Edwards, Alex Ray, Amos Storkey.

3 papers in our library · 2018–2022

OpenAlex · h-index 9 · 24 works · 2,632 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 2,946 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #1108

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

🏢OpenAISan Francisco · United States2018–2025 · 5 paperscurrent🎓University of TorontoToronto · Canada2008–2016 · 6 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).Computer Science · 3 worksArtificial Intelligence · 2 worksDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 workEfficient models & compression · 1 work

💡 Concepts 2Key ideas and methods explained on our concept pages.Language model · 1 workLarge language models · 1 work

 Works by year

2020s

20221 paper20211 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Evaluating Large Language Models Trained on Code (2021 · 2,659 citations)
    Code generation models were usually scored by how closely their output text matched a reference, for example with BLEU.
  2. Exploration by Random Network Distillation (2018 · 302 citations)
    Deep reinforcement learning agents often fail when rewards are sparse. Many existing curiosity-style or count-style exploration techniques are also hard to run across many parallel environments. The authors propose Random Network Distillation (RND). A fixed, randomly initialized target network maps each observation…
  3. Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets (2022 · 177 citations)
    Why heavily overparameterized networks generalize instead of simply memorizing their training data is still poorly understood.

All works

8 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Evaluating Large Language Models Trained on CodearXiv:2107.03374Mark Chen, Jerry Tworek, Heewoo Jun, et al.2021 · 2,659 citations
  2. Large-Scale Study of Curiosity-Driven Learning ↗arXiv:1808.04355 · no summary yetYuri Burda et al.2018 · 357 citations
  3. Importance Weighted Autoencoders ↗arXiv (Cornell University) · no summary yetYuri Burda, Roger Grosse, Ruslan Salakhutdinov2015 · 316 citations
  4. Exploration by Random Network DistillationarXiv:1810.12894Yuri Burda, Harrison Edwards, Amos Storkey, et al.2018 · 302 citations
  5. Grokking: Generalization Beyond Overfitting on Small Algorithmic DatasetsarXiv:2201.02177Alethea Power, Yuri Burda, Harri Edwards, et al.2022 · 177 citations
  6. Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing ↗arXiv (Cornell University) · no summary yetYuri Burda, Roger Grosse, Ruslan Salakhutdinov2014 · 43 citations
  7. On the Quantitative Analysis of Decoder-Based Generative Models ↗arXiv (Cornell University) · no summary yetYuhuai Wu et al.2016 · 41 citations
  8. Accurate and Conservative Estimates of MRF Log-likelihood using Reverse Annealing. arXiv:1412.8566 ↗no summary yetYuri Burda, Roger Grosse, Ruslan Salakhutdinov2014

 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 2,946 times (of the 149,439 papers whose bibliographies we read); 794 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/yuri-burda/