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Digital Science (United States)

United States

46 papers here623 citations across 46
fields
  • cs.CV7
  • cs.DL5
  • cs.CY4
  • quant-ph4
  • cs.DC3
  • cs.LG3
  • cs.AI2
  • cs.CL2
ROR 020h4b682OpenAlex

affiliations via OpenAlex

output
20122026
most citedResults of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction

313 citations

researchers with a paper here
  • Zhenjie Zhang2 profiles4 · h 36
  • Arcangelo Massari3 · h 6
  • Florian M. Savoy3 · h 12
  • S. Dev3 · h 29
  • Silvio Peroni3 · h 4
  • Stefan Winkler3 · h 28
  • Y. Lee3 · h 30
  • Chenkai Weng2 · h 17
  • C. Maple2 · h 49
  • Hua‐Lei Yin2 · h 30
  • Kay Atefi2 · h 1
  • Mengya Zhu2 · h 1
collaborating institutions
  • Advanced Digital Sciences CenterSG8 papers
  • University of Illinois Urbana-ChampaignUS7 papers
  • Nanyang Technological UniversitySG6 papers
  • Nanjing UniversityCN3 papers
  • University of BolognaIT3 papers
  • University of HullGB3 papers
  • Cisco Systems (United States)US2 papers
  • Collaborative Innovation Center of Advanced MicrostructuresCN2 papers
  • Cornell UniversityUS2 papers
  • Geospatial Research (United Kingdom)GB2 papers
  • Hamad bin Khalifa UniversityQA2 papers
  • LMU KlinikumDE2 papers
Showing 2026 · cs.AIShow all

1 paper · 2 filters

cs.AI2026

How Rules Represent Causal Knowledge: Causal Modeling with Probabilistic Logic Programming

Kilian Rueckschloss, Felix Weitkaemper

Pearl famously argues that causal knowledge enables the prediction of intervention effects. By contrast, purely descriptive knowledge supports only conclusions drawn from observati…

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