Skill UI

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 aiming to submit machine learning manuscripts to the Asian Conference on Machine Learning (ACML). This skill helps evaluate manuscript fit, re-frame research from journal or arXiv styles, understand required contribution types (e.g., algorithms, theorems, benchmarks), and align the paper's evidence bar with the expectations of a top-tier, regional AI/ML venue. Use it for venue selection and submission strategy development.

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 how to position, structure, and strengthen empirical Natural Language Processing (NLP) manuscripts for submission to the Conference on Empirical Methods in Natural Language Processing (EMNLP). It provides deep insights into conference fit, required evidence (baselines, error analysis, datasets), proper framing, and anticipating reviewer expectations for top-tier AI venues.

This guide helps authors position their machine learning systems manuscripts for the rigorous Conference on Machine Learning and Systems (MLSys). It details the venue's specific focus on system-level contributions, including training/inference infrastructure, compilers, data systems, and hardware-aware ML. Learn how to frame your work to appeal to systems reviewers by emphasizing measurable impacts on capability, cost, reliability, or deployment scale.

Provides an expert framework for authors submitting to the Conference on Robot Learning (CoRL). It guides writers on how to re-frame general computer science manuscripts into specialized, robust robot-learning narratives. The guide covers defining the contribution type, establishing the evidence bar (from simulation to real-world transfer), and strategically positioning the work against major robotics conferences like ICRA and IROS.

A specialized routing tool designed for academic researchers submitting manuscripts to top-tier Computer Science and AI conferences. It helps users navigate complex decision points, such as selecting the best venue (e.g., ICML vs NeurIPS), matching contribution types (theory, system, empirical study), and adhering to specific submission guidelines and review constraints.

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 comprehensive guide assists authors in strategically positioning their computer vision manuscripts for the European Conference on Computer Vision (ECCV). It covers conference fit assessment, manuscript re-framing, required evidence (e.g., ablations, baselines, failure modes), and structural advice to ensure the work aligns with ECCV's scope and current review policies. Use this tool for venue selection and submission preparation.

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.

A comprehensive guide for authors aiming to submit robotics and automation manuscripts to the IEEE ICRA. It provides detailed advice on conference fit, necessary evidence (hardware/simulation), paper framing, and structuring the work to meet the standards of top-tier robotics venues. It also helps differentiate ICRA from similar conferences like IROS and RSS.

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.