arXiv (All)
open access
On the Relation between Code Quality and Machine Learning Performance: A Large-scale Empirical Study
Context: Computational notebooks are the standard environment for machine learning (ML) development. Within the ML community, model performance is often the primary considered metric, and code quality is treated as a secondary concern. This prioritization relies on a largely untested assumption that
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Identical Runs, Different Results: Benchmarking AI Coding Agents on Open-Weight Models#1118885
- Identical Runs, Different Results: Benchmarking AI Coding Agents on Open-Weight Models#1108848
- Amazon’s Machine Learning Toolkit: Sagemaker#10785
- DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures#792673
- DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures#788673
Record · ID 997612
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive