a-comparison-you-never-run-defaults-to-your-preference
SolidUse at hypothesis drafting, study design and implementation when the task could plausibly be attacked by more than one family of method - hand-built features fed to a fitted model, a network trained on the raw structure or sequence, a pretrained backbone, retrieval - and your hypothesis list mostly compares variants inside one of them. Covers separating the hypotheses that would change what you build from the ones that would change an argument, requiring code on both sides of a family claim, running the comparison at a budget you actually have and reading each side's slope rather than its level, and demoting a comparison you will not run into a priced assumption.
Install
Quality Score: 82/100
Skill Content
Details
- Author
- tangxiangru
- Repository
- tangxiangru/AutoR
- Created
- 6 months ago
- Last Updated
- 2 weeks ago
- Language
- Python
- License
- NOASSERTION
Integrates with
Similar Skills
Semantically similar based on skill content — not just same category
cost-the-rung-you-need-not-the-cheapest-one-in-the-family
Use at literature survey, hypothesis generation and study design of a task that hands you a training split and an unlabelled test split, scores predictions by a fixed error metric, and has published best numbers for that dataset and metric, when you are about to decide that the kind of model behind those numbers does not fit your clock. Covers which rung of the ladder is worth timing at all, replacing "hours to finish the reference schedule" with "epochs until this passes what I already have", and pricing the first member of another family before the Nth member of this one.
chemistry-accuracy-and-cost-for-every-module-you-swap-in
Use at study design, through experimentation and again at analysis when the method under test is a drop-in replacement for a standard layer — a different basis, kernel, activation family or transform — and the source claims the replacement is both more accurate and cheaper. Covers giving every alternative module a cell in the accuracy column and in the cost column, fixing one matching convention across both, and dividing the runtime by the invariant already sitting in your own results file before you publish a contradiction of the source's ratio.
math-equal-effort-baselines-and-knob-sweeps
Use at study design when the source names competing algorithms and they are about to become a related-work paragraph instead of arms. Covers running every named baseline at equal tuning effort, and sweeping the parameter you claim credit for.