{"schemaVersion":1,"target":{"type":"reading","id":"5ea67a254e4c9601e7a00816fd4cdee657a5604eb5a2898172875d1fa6cec709","revision":"34d1f791-588d-436e-8416-bd8acbce7122","revisionKind":"publication-id","title":"arXiv cs.AI: Measuring deep learning performance across architectures and tasks","url":"https://escapemeridian.com/readings/arxiv-cs-ai-measuring-deep-learning-performance-across-5ea67a25"},"source":{"type":"source","id":"https://arxiv.org/abs/2610.38037","revision":"b4339eb1e3c332a6bb2d305d6bf607d87f35c339c3226a134c8fa870f681b412","revisionKind":"sha256-source-text","title":"Measuring Deep Learning Performance - An Empirical Study of Performance Distributions Across Architectures and Tasks","url":"https://arxiv.org/abs/2610.38037"},"status":"available","items":[],"next":null,"scope":"Explicit citations in public readings and public file attachments to this reading. Shared topics are not citations. Other artifact types need an explicit reference adapter."}