26 citations · 50 across the 10 of their papers we have counts for
4 papers · 1 filter
Croissant Baker: Metadata Generation for Discoverable, Governable, and Reusable ML Datasets
Rafi Al Attrach, Rajna Fani, Sebastian Lobentanzer +17
Croissant has emerged as the metadata standard for machine learning datasets, providing a structured, JSON-LD-based format that makes dataset discovery, automated ingestion, and re…
Croissant: A Metadata Format for ML-Ready Datasets
Mubashara Akhtar, Omar Benjelloun, Costanza Conforti +28
Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that crea…
On the Readiness of Scientific Data for a Fair and Transparent Use in Machine Learning
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
To ensure the fairness and trustworthiness of machine learning (ML) systems, recent legislative initiatives and relevant research in the ML community have pointed out the need to d…
A domain-specific language for describing machine learning datasets
Joan Giner-Miguelez, Abel Gómez, Jordi Cabot
Datasets play a central role in the training and evaluation of machine learning (ML) models. But they are also the root cause of many undesired model behaviors, such as biased pred…