chemistry-group-attribution-over-the-split-and-the-baseline-mode
SolidUse at study design, experimentation and analysis when the deliverable includes which substructures, functional groups or motifs drive the model's predictions, once the attribution estimator is already chosen. Covers widening from the one molecule the source drew to the whole evaluation split with per-molecule normalisation, running the identical attribution on the comparator model so a claim of better interpretability becomes measurable, and treating a learned edge or subgraph mask as a first-class output.
Install
Quality Score: 85/100
Skill Content
Details
- Author
- tangxiangru
- Repository
- tangxiangru/AutoR
- Created
- 5 months ago
- Last Updated
- today
- Language
- Python
- License
- NOASSERTION
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
the-attribution-is-the-deliverable
Use when the task statement names interpretability, explainability, feature importance, saliency or attribution among its outputs or objectives. The graded artifact is then the attribution map itself — per input unit, by the field's standard estimator, drawn as a figure — not a diagnostic about the model's internals and not an argument that the model is uninterpretable.
chemistry-a-cut-variant-takes-its-analyses-with-it
Use at literature survey, at study design, at every descope decision and again at writing, when the source's method is a family — the same module dropped into two or more backbones, or one architecture published in several named variants — and you are about to run only one of them. Covers listing which of the source's downstream analyses were produced from which variant before any of them is cut, shrinking a variant rather than deleting it, and what a saliency map, case study or ablation computed on the surviving variant is and is not evidence for.
neuroscience-comparator-ladder-and-per-unit-predictions
Use at study design and analysis when a model is about to be compared against one alternative, or a fit reported without a negative control. Covers the two-sided comparator ladder, the control representation panel, and splitting per-unit predictions into the ones a measurement validates and the ones that stay predictions.