iclr-experiments
De la autor
Use when designing or auditing ICLR experiments, including baselines, ablations, scaling laws, robustness, statistics, benchmarks, human evaluation, and compute reporting. Use when a reviewer questions whether a representation-learning or model gain is real, when you must isolate one mechanism with an ablation, or when preparing a small compute-matched control that can be posted inline during the public discussion period.
Instalare
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill iclr-experiments -a claude-code
Se instalează în .claude/skills/iclr-experiments/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
Identic octet cu octet cu commit-ul scanat, cu licența depozitului și un fișier SOURCE.txt.
Vrei ca agentul să-ți propună skill-uri în timp ce scrii? Vezi cum folosești skill-urile din IDE.
Verificări de siguranță pe SKILL.md
Niciun avertisment.
De ce 88
aiqbench calculează scorul de încredere din semnale publice. Nu este un audit de securitate, așa că citește un skill înainte să-l instalezi.
Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
- acl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing experiments for an ACL paper, covering tuned LLM baselines, multi-dataset and multilingual evaluation, statistical significance and variance, human evaluation with agreement reporting, contamination and prompt-sensitivity controls, ablations, and error-analysis expectations in NLP reviewing.Știință★ 1.3K
- cvpr-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the experimental program of a CVPR paper, covering benchmark and baseline selection under matched-compute fairness, the ablation study reviewers treat as mandatory, qualitative and failure-case evidence, efficiency metrics tied to the Compute Reporting Form, and generalization tests beyond a single dataset.Testare★ 1.3K
- icml-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when stress-testing ICML experimental evidence before submission or rebuttal, including strong tuned baselines, mechanism-isolating ablations, seed variance and confidence intervals, compute disclosure, data leakage and split construction, reproducibility, negative results, and fit to ICML soundness, originality, and significance scoring.Testare★ 1.3K
- icra-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the experimental section of an ICRA paper — real-robot versus simulation-only evidence, trial counts and success-rate reporting, task distributions and resets, baseline fairness on shared hardware, sim-to-real transfer claims, failure-mode analysis, and the statistics robotics reviewers actually expect.Știință★ 1.3K
- corl-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing experiments for a CoRL robot-learning paper — seeds and evaluation-episode counts, task-suite breadth, real-robot versus simulation evidence, sim-to-real gap measurement, baseline fairness across BC/RL/VLA families, generalization splits, and statistics for success-rate claims.Știință★ 1.3K
- icse-experimentsbrycewang-stanford/Awesome-Journal-SkillsUse when designing or auditing the evaluation of an ICSE research-track paper, covering subject and benchmark selection, baseline fairness, statistical tests and effect sizes as SE reviewers expect them, qualitative-methods rigor, ablations for AI-based techniques, and threats-driven study design.Testare★ 1.3K
Note și recenzii
—
nicio recenzie
Ai încercat acest skill? Notează-l ca să-i ajuți pe alții. Autentifică-te
Discuție
Intră în discuție. Autentifică-te