3 papers
cs.AI2026
Croissant Tasks: A Metadata Format for Reproducible Machine Learning Evaluations
Omar Benjelloun, Leonardo Martins Bianco, Isabelle Guyon +8
Reproducibility is fundamental to the scientific method, yet remains a critical challenge in machine learning. Contributing factors include underspecified execution details and bri…
cs.LG2026
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…
cs.AI2025
AI Benchmark Democratization and Carpentry
Gregor von Laszewski, Wesley Brewer, Jeyan Thiyagalingam +28
Benchmarks are a cornerstone of modern machine learning, enabling reproducibility, comparison, and scientific progress. However, AI benchmarks are increasingly complex, requiring d…