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Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
4 references·4 citing·32 second hop
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering (2019)Provenance Data in the Machine Learning L…A Survey on Collecting, Managing, and Analyzing Provenance from Scripts (2019)A Survey on Collecting, Manag…Data Lifecycle Challenges in Production Machine Learning (2018)Data Lifecycle Challenges in …A survey on provenance: What for? What form? What from? (2017)A survey on provenance: What …MEX vocabulary (2015)MEX vocabularyPROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems (2024)PROV-IO: A Cross-Platform Pro…Common Metadata Framework: Integrated Framework for Trustworthy Artificial Intelligence Pipelines (2024)Common Metadata Framework: In…PROV-IO (2022)PROV-IOBraid-DB: Toward AI-Driven Science with Machine Learning Provenance (2022)Braid-DB: Toward AI-Driven Sc…Experiment Databases: Towards an Improved Experimental Methodology in Machine Learning (2007)The design and realisation of the Virtual Research Environment for social sharing of workflows (2008)Wings: Intelligent Workflow-Based Design of Computational Experiments (2010)A Semantic Framework to Support AI System Accountability and Audit (2021)Machine Learning Pipelines: Provenance, Reproducibility and FAIR Data Principles (2021)Workflow provenance in the lifecycle of scientific machine learning (2021)Beyond Accuracy: What Data Quality Means to Data Consumers (1996)Taverna: a tool for the composition and enactment of bioinformatics workflows (2004)Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010)The fallacy of inscrutability (2018)FAIR Computational Workflows (2019)This Thing Called Fairness (2019)A cost-based model and effective heuristic for repairing constraints by value modification (2005)A multidisciplinary survey on discrimination analysis (2013)Holistic data cleaning: Putting violations into context (2013)Exploring How Machine Learning Practitioners (Try To) Use Fairness Toolkits (2022)A Survey of Data Quality Requirements That Matter in ML Development Pipelines (2023)Systematic review of data-centric approaches in artificial intelligence and machine learning (2023)Why Most Published Research Findings Are False (2005)Why Most Published Research Findings Are False (2005)Snakemake—a scalable bioinformatics workflow engine (2012)Capturing and querying fine-grained provenance of preprocessing pipelines in data science (2020)dtoolAI: Reproducibility for Deep Learning (2020)A survey of data provenance in e-science (2005)The Open Provenance Model core specification (v1.1) (2010)Data Management Challenges in Production Machine Learning (2017)Building the I (Interoperability) of FAIR for Performance Reproducibility of Large-Scale Composable Workflows in RECUP (2023)Provenance and scientific workflows (2008)The Fourth Paradigm: Data-Intensive Scientific Discovery [Point of View] (2011)SPADE: Support for Provenance Auditing in Distributed Environments (2012)ProvLight: Efficient Workflow Provenance Capture on the Edge-to-Cloud Continuum (2023)Constructing a metadata knowledge graph as an atlas for demystifying AI pipeline optimization (2025)
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References

  • 2017A survey on provenance: What for? What form? What from?298 cited
  • 2018Data Lifecycle Challenges in Production Machine Learning213 cited
  • 2015MEX vocabulary54 cited
  • 2019A Survey on Collecting, Managing, and Analyzing Provenance from Scripts47 cited

Cited by

  • 2024PROV-IO: A Cross-Platform Provenance Framework for Scientific Data on HPC Systems10 cited
  • 2024Common Metadata Framework: Integrated Framework for Trustworthy Artificial Intelligence Pipelines9 cited
  • 2022PROV-IO9 cited
  • 2022Braid-DB: Toward AI-Driven Science with Machine Learning Provenance6 cited

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