Skill UI

This guide provides a comprehensive framework for stress-testing Machine Learning experimental evidence before submission or rebuttal. It covers critical areas such as establishing strong baselines, conducting mechanism-isolating ablations, reporting variance, checking for data leakage, and disclosing compute costs. Use this to ensure your ML claims are scientifically sound, robust, and fair against rigorous academic scrutiny.

This guide provides structured methodologies for authors to position their research within the context of existing literature and concurrent submissions at top-tier machine learning conferences (e.g., ICML, NeurIPS). It emphasizes identifying the technical delta, addressing novelty concerns, and properly citing related work to maximize acceptance chances.

A comprehensive guide for researchers detailing how to strengthen the reproducibility of AI/ML papers for top-tier conferences like ICML. It provides an evidence checklist covering data sources, anonymized code, random seeds, compute budget disclosure, and theoretical assumptions, ensuring claims are robust and verifiable.

A comprehensive audit checklist for ensuring final submissions to the ICML conference. It verifies compliance across critical areas including LaTeX formatting, strict page limits, double-blind anonymity, impact statements, dual submission rules, and adherence to the latest LLM and ethical policies. Essential for researchers preparing papers for top-tier AI venues.

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.

This skill serves as a comprehensive router for managing the entire lifecycle of an ICML manuscript. It guides users through every phase, including topic selection, drafting, artifact preparation, OpenReview submission, double-blind review, author response, and final camera-ready publication. It helps track risks, evidence, and official conference policies.

This comprehensive guide teaches researchers how to structure and refine machine learning papers for top-tier conferences like ICML. It focuses on achieving clarity, soundness, originality, and significance within strict page limits, providing actionable strategies to ensure the research withstands rigorous reviewer scrutiny.

A comprehensive guide for authors preparing the 'Related Work' section for top-tier robotics conferences like ICRA. It details how to conduct thorough literature reviews across multiple research 'lanes,' handle concurrent work (arXiv), properly self-cite under double-anonymous rules, and position the novelty against both academic rivals and commercial systems.

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.

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 skill provides a comprehensive guide for authors submitting papers in biomedical imaging algorithms, microscopy, and medical imaging to top-tier conferences like ISBI. It helps authors strategically frame their work, assess scope fit, ensure strong evidence, and understand the unique requirements of the venue, ensuring the manuscript is optimized for conference review standards.

A comprehensive guide for authors targeting the IEEE-RAS International Conference on Humanoid Robots. This skill helps frame, scope, and structure research papers in humanoid robotics. It dictates the necessary level of hardware evidence, whole-body control integration, and perception depth required to succeed at this top-tier venue, while distinguishing it from general robotics conferences like ICRA or RoboCup.