Skill UI

A comprehensive guide for researchers deciding the optimal venue for a computer vision project. It analyzes the biennial nature of top conferences like ICCV, evaluating project readiness, timing maturity, and comparing the unique requirements of ICCV, CVPR, and ECCV to maximize acceptance chances.

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.

A comprehensive guide for authors preparing submissions for top-tier computer vision conferences like ICCV. It instructs writers on structuring papers for a generalist reviewer pool, maintaining the 'inclusive 8-page economy,' adopting a mechanism-first exposition style, and formulating claims that withstand rigorous review and rebuttal cycles. Essential for elevating technical prose to a publishable standard.

This guide outlines the standards for ensuring the verifiability and rigor of theoretical computer science papers, particularly for venues like ICDT. It mandates complete proofs, precise pinning of models and assumptions, explicit matching of upper and lower bounds, and strict internal consistency across all claims and full versions. Use this checklist before submission to elevate academic quality.

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.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.

This guide provides a comprehensive framework for stress-testing Machine Learning experimental evidence before submission or rebuttal. It covers critical areas such as establishing strong baselines, conducting mechanism-isolating ablations, reporting variance, checking for data leakage, and disclosing compute costs. Use this to ensure your ML claims are scientifically sound, robust, and fair against rigorous academic scrutiny.

A comprehensive guide for researchers detailing how to strengthen the reproducibility of AI/ML papers for top-tier conferences like ICML. It provides an evidence checklist covering data sources, anonymized code, random seeds, compute budget disclosure, and theoretical assumptions, ensuring claims are robust and verifiable.

A comprehensive guide for authors targeting the IEEE Conference on Virtual Reality and 3D User Interfaces (IEEE VR). It details the strategic framing, required evidence (e.g., measured immersive user experience), proper positioning, and necessary structural adjustments to ensure a computer science manuscript is highly relevant and competitive for this specialized venue.

This skill serves as a comprehensive guide for positioning computer communications and networking manuscripts for the IEEE INFOCOM conference. It helps authors understand the venue's specific scope, the required evidence bar (solid analytical or empirical performance evidence), and how to re-frame their work to align with INFOCOM's standards compared to sibling venues like SIGCOMM or CoNEXT. Use this for submission strategy and manuscript calibration.

A comprehensive guide for authors submitting data mining research to elite computer science venues like ICDM. This skill helps define the paper's fit, target the correct scope (algorithms, graph mining, etc.), and structure the manuscript to meet rigorous academic standards. It emphasizes providing sufficient evidence, rigorous baselines, and clear positioning relative to the conference's focus.

This guide assists authors with positioning their computer science manuscripts for the IEEE International Conference on Software Maintenance and Evolution (ICSME). It details the core scope, including software maintenance, evolution, refactoring, and technical debt, and provides best practices for structuring submissions. It emphasizes the need for real repository evidence, actionable engineering insights, and proper differentiation from related venues like SANER or ICSE.