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Learning from Big Code: Probabilistic Models, Analysis and Synthesis

Learning from Big Code: Probabilistic Models, Analysis and Synthesis is a science project.

Event research_grant eu_cordis:680358
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Key Facts

  • Start time: April 1, 2016[1]
  • End time: March 31, 2021[2]
  • Participant: Probabilistic Models, Analysis and Synthesis — participant (P710): ETH Zurich[3]
  • Short name: BIGCODE[4]
  • Budget: 1,500,000 euro[5]
  • Funding scheme: Probabilistic Models, Analysis and Synthesis — funding scheme (P6195): Horizon 2020[6]

References

Programmatic citations — every numbered marker resolves to a verifiable graph row below.

Direct Wikidata claims

  1. [1] ↑ . wikidata.org.
  2. [2] ↑ . wikidata.org.
  3. [3] ↑ . wikidata.org.
  4. [4] ↑ . wikidata.org.
  5. [5] ↑ . wikidata.org.
  6. [6] ↑ . wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Learning from Big Code: Probabilistic Models, Analysis and Synthesis. Retrieved October 8, 2026, from https://4ort.xyz/entity/learning-from-big-code-probabilistic-models-analysis-and-synthesis
MLA “Learning from Big Code: Probabilistic Models, Analysis and Synthesis.” 4ort.xyz Knowledge Graph, 4ort.xyz, 8 Oct. 2026, https://4ort.xyz/entity/learning-from-big-code-probabilistic-models-analysis-and-synthesis.
BibTeX @misc{4ortxyz_learning-from-big-code-probabilistic-models-analysis-and-synthesis_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Learning from Big Code: Probabilistic Models, Analysis and Synthesis}}, year = {2026}, url = {https://4ort.xyz/entity/learning-from-big-code-probabilistic-models-analysis-and-synthesis}, note = {Accessed: 2026-10-08}}
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