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institution

Defence Science and Technology Laboratory

United Kingdom

10 papers here136 citations across 10
fields
  • astro-ph.IM3
  • astro-ph.EP2
  • cs.CV1
  • cs.LG1
  • physics.acc-ph1
  • physics.app-ph1
  • physics.optics1
ROR 04jswqb94OpenAlex

affiliations via OpenAlex

output
20192025
most citedAssuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

87 citations

researchers with a paper here
  • P. Chote2 profiles5 · h 23
  • D. Pollacco2 profiles4 · h 65
  • Grant Privett2 profiles3 · h 2
  • W. Feline3 · h 6
  • B. Cooke2 · h 17
  • James A. Blake2 · h 4
  • J. McCormac2 · h 33
  • Robert Airey2 · h 3
  • S. Eves2 · h 5
  • Simon George2 profiles2 · h 2
  • A. Ash1 · h 18
  • A. Greenwood1 · h 2
collaborating institutions
  • University of WarwickGB5 papers
  • Queen's University BelfastGB3 papers
  • Health Awareness (United States)US2 papers
  • University of SheffieldGB2 papers
  • University of YorkGB2 papers
  • Brera Astronomical ObservatoryIT1 paper
  • Czech Technical University in PragueCZ1 paper
  • Department of SpaceIN1 paper
  • Extreme Light Infrastructure - Nuclear PhysicsRO1 paper
  • Horia Hulubei National Institute for R and D in Physics and Nuclear EngineeringRO1 paper
  • Imperial College LondonGB1 paper
  • John Adams Institute for Accelerator ScienceGB1 paper
Showing 2019Show all

1 paper · 1 filter

cs.LG2019★ 87 cited

Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges

Rob Ashmore, Radu Calinescu, Colin Paterson

Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed ma…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.