Skill UI

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.

A comprehensive guide designed to help researchers determine the optimal conference or journal venue (e.g., AAMAS, NeurIPS, ICML, EC) for multi-agent AI research. It emphasizes that the contribution must center on the interaction result—such as mechanism design, coordination, or negotiation—rather than simply applying a single-agent method in a multiagent testbed. This tool sharpens the research framing before writing begins.

This guide helps authors assess the suitability of their computer science manuscripts for the ACM International Conference on Multimedia (ACM MM). It provides detailed strategies for framing, highlighting multimodal contributions, structuring evidence, and aligning the work with top-tier academic conference expectations in multimedia retrieval, multimodal learning, and media applications.

A comprehensive guide for authors preparing manuscripts for the International Conference on Artificial Intelligence and Statistics (AISTATS). It advises on whether the paper's contribution is statistically grounded and theory-backed, guiding framing, evidence presentation, and understanding reviewer expectations in statistical machine learning.

A comprehensive guide for authors targeting the British Machine Vision Conference (BMVC). This skill helps assess whether a computer vision manuscript fits the venue, providing expert advice on framing, necessary evidence gaps, structural improvements, and differentiating the work from related CV conferences (e.g., CVPR, ICCV). It ensures the contribution is clear and highly specialized for specialist reviewers.

This skill provides comprehensive guidance on positioning, structuring, and refining computer vision manuscripts for top-tier venues like CVPR. It covers identifying major novelty, required experimental rigor (e.g., ablations, failure modes), framing contributions, and adhering to specific conference submission standards for CV, VLM, and recognition tasks. Use it to refine your submission strategy and ensure your work meets the high bar set by leading AI conferences.

This skill guides authors on strategically positioning their AI/ML manuscripts for the European Conference on Artificial Intelligence (ECAI). It provides deep insights into the conference's scope, required contribution framing (e.g., algorithm, system, theorem), evidence standards, and how to tailor the narrative for a general European AI readership. Use it when targeting ECAI or needing to refine a paper from a journal or arXiv style into a selective conference submission.

This guide helps researchers determine the most appropriate academic venue for their responsible AI work. It provides detailed criteria and rigorous testing methods (like the Delete-the-equity test) to assess whether fairness, accountability, or transparency is a primary, core contribution, guiding authors away from merely 'bolted-on' ethical framing.

This comprehensive guide helps researchers position their work in Automatic Speech Recognition (ASR) and Spoken Language Understanding (SLU) for the IEEE ASRU workshop. It covers conference fit assessment, manuscript re-framing strategies, understanding venue-specific evidence requirements (e.g., decoding, robustness analysis), and differentiating the contribution from related fields like Interspeech or SLT. Use this tool for submission strategy and academic positioning.

This guide provides comprehensive strategic advice for authors submitting manuscripts related to Artificial Intelligence, Constraint Programming, and Operations Research (AI/OR) to top-tier conferences like CPAIOR. It helps refine scope, structure, evidence standards, and narrative framing to meet highly specialized reviewer expectations, ensuring the paper is positioned as a solid hybrid optimization contribution.

This resource guides authors on positioning their research for the International Conference on Automated Machine Learning (AutoML). It helps evaluate if a manuscript—especially those involving NAS, HPO, or meta-learning—is a suitable fit for the venue. The focus is on framing contributions that enhance the automated search process, robust methodologies, and system pipelines, rather than solely relying on final leaderboard scores.

A comprehensive guide for authors targeting top-tier conferences like ICAPS. It assists in re-framing automated planning and scheduling manuscripts, providing detailed advice on structuring the contribution, identifying necessary empirical/theoretical evidence, optimizing scope vocabulary, and strategically positioning the work against related venue submissions. Use this tool for venue selection, manuscript calibration, and strengthening the submission narrative.