TRAINING DEEP NEURAL NETWORKS ON NOISY LABELS WITH BOOTSTRAPPING
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Classifier prediction
metacan-v1-d91a1de5be90Predictions imitate two machine teachers. Scores are not calibrated prevalence probabilities.
- Classifier candidate
- Simulation or modellingTheoretical or conceptual
- Classifier consensus
- Simulation or modelling
Teacher imitation scores
Codex
Gemma
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.049 how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baselineverbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.