Skill UI

This comprehensive workflow guides users through the end-to-end process of utilizing cloud-based GPU computing resources on Vast.ai. It covers searching for ideal GPU offers based on specs and price, provisioning an instance, running complex training or inference jobs via SSH, collecting model artifacts, and crucially, destroying the instance to prevent unexpected billing. Ideal for deep learning training and research.

Creates high-fidelity visuals through structured JSON prompts and a Python generator script, handling subjects, styles, references, and technical specs for characters, scenes, or products.

This comprehensive guide establishes a rigorous framework for ensuring reproducibility in robot learning research. It details how to document both the "rerunnable half" (software, simulation versions, training configs, seeds) and the "auditable half" (physical hardware specs, sensors, firmware). Adhering to these standards allows experts to verify and rebuild the experimental results.

A comprehensive guide on preparing and structuring evidence packages for recommender system research, especially for academic submissions. It details how to ensure that all ranking claims, off-policy estimates, and evaluation metrics are end-to-end reproducible by providing artifacts like datasets, split scripts, model checkpoints, and propensity logs in an anonymous, verifiable repository.

A comprehensive guide for structuring the Related Work section for top-tier recommender systems conferences (RecSys). It advises authors on auditing novelty by positioning their contribution against adjacent research fields (e.g., SIGIR, KDD, WSDM), addressing critical lines (like reproducibility and off-policy evaluation), and ensuring the work is technically distinct from prior or concurrent studies.

This comprehensive audit checklist is designed for authors submitting papers to the ACM RecSys conference. It guides users through selecting the correct specialized track (Main, Industry, Reproducibility), adhering to strict page budgets, ensuring thorough double-blind anonymization, and mitigating common submission risks like identity leaks and improper data splitting.

A comprehensive guide for revising academic papers in recommendation systems (RecSys). It covers best practices for structuring the first page, ensuring rigorous evaluation protocols (e.g., temporal splits, equal-budget baselines), and avoiding methodological overclaims, helping authors meet the standards of top-tier AI conferences.