Skill UI

A comprehensive guide for authors submitting AI/ML papers to the International Conference on Learning Representations (ICLR). It helps authors position the work, optimize framing, assess evidence strength, and ensure the manuscript adheres to the rigorous standards of deep learning and representation learning required for a top-tier, open-review conference.

This guide provides a comprehensive framework for authors targeting elite AI/ML conferences like ICML. It covers academic positioning, identifying core ML contributions, structuring the narrative (Problem, Why Now, What is New), and establishing a rigorous evidence bar, including baselines and ablations. It also advises on distinguishing the work from closely related venues (e.g., NeurIPS, ICLR).

A comprehensive guide for authors targeting the International Conference on Pattern Recognition (ICPR). This skill helps evaluate if a manuscript fits the venue's scope, covering core topics like pattern recognition, computer vision, biometrics, and machine learning. It details necessary framing, evidence requirements (e.g., ablations, limitations), and how to differentiate the work from similar top-tier conferences (e.g., CVPR, ICML).

This guide establishes standards for reporting reproducibility in robotics research, addressing the difference between traditional ML and physical systems. It mandates that every claim must be classified as either 'rerunnable' (executable code/simulation) or 'auditable' (detailed logs, hardware specs, and protocols). It covers creating a complete evidence map, detailing the hardware specification ledger, explicitly reporting the sim-to-real gap, and writing an honest availability statement, all within the main paper body.

This skill guides the generation of a professional and highly structured revision response package for top-tier academic journals. It specifically addresses multi-round, double-blind review processes, ensuring every reviewer comment is addressed point-by-point. The output includes a summary statement, a detailed response table, and the marked revision manuscript, all while adhering to strict rules against identity leakage, making it ideal for streamlining the academic publishing workflow.

This guide provides comprehensive methodological frameworks for conducting rigorous empirical analyses in international trade and macroeconomics, particularly tailored to the standards of top-tier journals like the JIE. It covers assembling bilateral trade and cross-country panels, estimating gravity models using PPML, implementing local projections, and calibrating structural open-economy models. It ensures analyses are robust, replicable, and address common referee criticisms.

This guide provides comprehensive, advanced econometric strategies for ensuring robust identification in international economics manuscripts, specifically for high-impact journals. It covers modern approaches for gravity models (using PPML), staggered trade-policy shocks (Callaway-Sant'Anna), shift-share analyses, and structural open-economy modeling. It helps authors stress-test their research designs against rigorous academic standards.

A comprehensive guide for revising academic manuscripts for top-tier law and economics journals. It teaches authors how to restructure the introduction and abstract to follow the law-and-economics arc, ensuring the prose is plain, direct, and seamlessly integrates legal doctrines with rigorous economic analysis for a dual audience.

This guide details the comprehensive workflow for moving a manuscript accepted by Journal on Communications (JOC) from the acceptance stage to final publication. It covers crucial steps like meticulous proofreading, finalizing the layout, paying publication fees, signing necessary copyright agreements, and managing the publication timeline, ensuring authors navigate academic publishing compliance seamlessly.

This comprehensive skill guides authors through the specific submission requirements and editorial culture of the Journal of Machine Learning Research (JMLR). It helps authors determine if their ML method, theory paper, or dataset is appropriate for JMLR's high standards—emphasizing rigor, complete proofs, open code, and lasting scientific contribution over novelty signaling. Use this when targeting JMLR or comparing it against other top-tier ML venues.

A comprehensive guide for structuring the empirical section of high-level machine learning papers (e.g., KDD). It details the 'four-axis evidence plan'—Quality, Scale, Efficiency, and Mechanism—and emphasizes critical data hygiene practices. Learn how to prevent temporal leakage, structure ablations in a matrix format, and design post-launch measurements to ensure academic rigor and reproducibility.

A comprehensive guide for authors applying machine learning techniques to healthcare. It helps authors frame their research to meet the high standards of top ML4H conferences, covering biomedical data leakage, confounding effects, required evidence standards, and optimal submission strategies.