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).

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.