Skill UI

Provides a comprehensive guide for planning strategy within major academic conference review systems, such as OpenReview. It details the double-blind review lifecycle, author response strategies, managing public vs. private discussions, and understanding how permanent visibility impacts decision-making. Ideal for authors and reviewers preparing for high-stakes academic submissions.

This guide provides a comprehensive audit checklist for submissions to major AI/ML conferences like ICLR. It covers critical areas including maintaining double-blind anonymity, adherence to page limits, required LLM disclosure, and mitigating desk-reject risks. Use this before submission deadlines to ensure your paper is fully compliant and ready for OpenReview.

This guide outlines best practices for structuring academic papers submitted via OpenReview, particularly for venues like ICLR. It advises authors on where to place different types of evidence—such as core claims, full derivations, code, and large datasets—to ensure the central argument remains visible while maintaining academic rigor. It covers strategies for managing annexes, maintaining anonymity, and updating revisions.

This guide helps researchers determine if their Machine Learning project is suitable for ICLR, or if a different top-tier conference (such as NeurIPS, ICML, or CVPR) is a better fit. It emphasizes the need for deep representation-learning insights, mechanistic understanding, and theoretical contributions over mere application performance.

This skill manages the end-to-end project lifecycle for academic conference submissions, specifically tailored for major venues like ICLR. It provides a structured timeline, defining critical milestones from initial topic selection and abstract submission through paper deadlines, public discussion, and final camera-ready versions. Use it to sequence research milestones, manage evidence paths, and ensure compliance with conference rules like anonymity audits and strict deadlines.

This guide helps transform technically sound machine learning drafts into highly readable, conference-ready papers suitable for ICLR and OpenReview. It emphasizes structuring the core learning-representation insight upfront, clarifying contribution types (method, theory, benchmark), optimizing for public skimming, and providing clear reviewer navigation paths. Use this when ensuring maximum impact and clarity in a challenging academic review environment.

A comprehensive guide for researchers deciding the best venue for their machine learning manuscript. It helps evaluate whether a paper is a strong fit for ICML, or if it should be rerouted to specialized tracks, other top-tier conferences (NeurIPS, ICLR), or academic journals based on its core contribution type (theory, method, application) and novelty.

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).

This skill provides a comprehensive strategic guide for authors targeting top-tier AI conferences like IJCAI. It assists in framing the manuscript's contribution, ensuring broad intellectual relevance across learning, reasoning, and planning, and structuring evidence to meet the expectations of cross-area AI specialists. Use it to optimize your submission strategy and compare it against neighboring venues (e.g., AAAI, ICLR).

This skill guides authors on how to strategically position their machine learning research against complex academic landscapes. It covers comparing work to neighboring conferences (ICML, ICLR), analyzing contemporaneous preprints, and defining the exact technical delta from prior methods to minimize novelty risk for top-tier AI venues.