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Amos Storkey

Amos Storkey has 2 papers in our library, in 2018–2026, most of them on Summarization, Efficient models & compression; often with Boyang Xue, Hongru Wang, Jeff Z. Pan.

2 papers in our library · 2018–2026

OpenAlex · ORCID · Wikidata · h-index 47 · 231 works · 25,422 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 96 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #5527

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

🎓University of EdinburghEdinburgh · United Kingdom1999–2025 · 133 paperscurrent🎓Imperial College LondonLondon · United Kingdom1996–1999 · 5 papers

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 2Research subjects and languages the work is about.Efficient models & compression · 1 workSummarization · 1 work

 Works by year

2020s

20261 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. 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…
  2. Self-Sum: Teaching an Agent to Decide Itself When and What to Summarize (2026)
    Agents that tackle long tasks pile up reasoning steps, actions and observations. Their context grows noisy and expensive to process. Common fixes compress history on a fixed schedule or hand the job to an external model. These approaches ignore where the agent actually is in the task and can discard crucial details.…

All works

6 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. The 2005 PASCAL Visual Object Classes Challenge ↗Lecture notes in computer science · no summary yetMark Everingham et al.2006 · 743 citations
  2. Probabilistic inference for solving discrete and continuous state Markov Decision Processes ↗International Conference on Machine Learning · no summary yetMarc Toussaint, Amos Storkey2006 · 484 citations
  3. Large-Scale Study of Curiosity-Driven Learning ↗arXiv:1808.04355 · no summary yetYuri Burda et al.2018 · 357 citations
  4. Exploration by Random Network DistillationarXiv:1810.12894Yuri Burda, Harrison Edwards, Amos Storkey, et al.2018 · 302 citations
  5. Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment ↗Lecture notes in computer science · no summary yetMark Everingham et al.2006 · 218 citations
  6. Self-Sum: Teaching an Agent to Decide Itself When and What to SummarizeFindings of the Association for Computational LinguisticsHongru Wang, Rui Wang, Jushi Kai, et al.2026

 Co-authors here

7 co-authors 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 96 times (of the 149,439 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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