Skill UI

A comprehensive guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

This skill provides authors with a comprehensive guide to positioning their computer science manuscript for the AAAI/ACM Conference on AI, Ethics, and Society (AIES). It covers critical aspects such as framing the contribution, meeting evidence standards, addressing social impacts, and distinguishing the work from similar venues like FAccT. Use it to refine submissions focused on AI ethics, governance, and socio-technical methods.

This guide provides comprehensive strategies for writing the Related Work section for submissions to AAAI. It teaches authors how to robustly position their paper's novelty by distinguishing contributions from both established archival literature and contemporaneous non-archival work (like arXiv papers). It also addresses specific reviewer expectations across various AI subfields (NeurIPS, ICML, ICLR) and outlines clear structural templates for maximum impact.

Explains AAAI's two-phase review process, Phase 1 rejection risk, Phase 2 additional reviews, AI-assisted review handling, author feedback, and stage-by-stage planning for submissions.

This guide helps researchers strategically decide if their AI project is a strong fit for the AAAI conference. It provides detailed criteria, including strong and weak signals, and routing logic to determine whether the work should be submitted to AAAI, or routed to specialized venues like NeurIPS, ICML, or domain-specific conferences (e.g., CVPR, KDD). Use it to refine your project's framing and maximize submission success.

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

This guide provides comprehensive instructions for preparing and submitting supplementary material for computer vision conferences like ECCV. It details how the supplement should support the main paper's claims without adding new results, covering topics such as qualitative result visualization, detailed ablation studies, code archive scrubbing, and ensuring full anonymity across all uploaded assets.

This guide outlines the complete project management workflow for major computer vision conferences like ECCV, CVPR, and ICCV. It covers the entire academic cycle, from initial registration and submission through peer review, acceptance, camera-readiness, and post-conference retargeting strategies. Useful for research teams planning multiple submissions across even and odd years.

This comprehensive guide outlines the critical steps required for authors preparing an accepted paper for ACM FAccT proceedings. It details the transition from anonymous drafts to the final, published 'camera-ready' format, covering mandatory de-anonymization (restoring author details and adding Positionality statements), switching to the specific ACM metadata standards (ORCID, CCS concepts), completing required documentation (datasheets/model cards), and adhering to complex publication timelines and rights requirements.

A comprehensive guide for researchers managing the complex, year-long cycle of major computer vision conferences (like ICCV). Covers critical stages from the initial 'autumn fork' decision and submission deadlines to rebuttal, final decisions, and post-conference strategies, including international travel logistics and fallback plans.

This guide provides comprehensive best practices for designing and auditing empirical experiments for top-tier ML conferences. It covers necessary ablations, robustness checks (e.g., data shift, seeds), fair baselines, and detailed compute reporting to ensure that reported gains are scientifically genuine, reproducible, and address common reviewer skepticism.