Turn a published ML replication into a bounded next check
Which specific claim in an existing label-smoothing or noisy-label replication can be mapped to accessible code, data and an affordable evaluation?
Make this plan your own ↓Read, edit and export without an account. This is preparation; no run or result is claimed.
What to compare
Read the full selected paper before defining the claim. A README, abstract, original author's score, or catalog entry cannot substitute for an independently specified evaluation plan.
Useful outputs
- A claim-to-source map preserving original authors, venue and version DOI.
- Separate code, data, weights and manuscript license findings.
- One scoped protocol with environment, input digests, metric, tolerances and a maximum execution quote.
Inputs and prerequisites
- Full-text claim review, exact artifact revisions and file-specific rights remain necessary.
- GPU/dependency/model/data requirements are outside this pilot; qualify and quote before execution.
What this work would not establish
- No replication was performed by importing these references.
- Do not rebrand an upstream article as a new Executable Science publication.
Start with these sources.
[Re] When Does Label Smoothing Help?
A replication concerning label smoothing in neural-network training.
Learning with Noisy Labels [~Re]visited
A replication report on learning from labels that contain errors, with source metadata identifying a Python deep-learning implementation.
[Re] Numerical influence of ReLU'(0) on backpropagation
A replication about the numerical influence of the derivative convention for ReLU at zero during backpropagation.
1.16. Probability calibration
A probability-calibration guide covering reliability curves and sigmoid, isotonic and temperature-scaling approaches.
3.4. Metrics and scoring: quantifying the quality of predictions
A broad scoring reference that distinguishes classification, regression, ranking and clustering metrics and their scorer interfaces.
Make the question your own.
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