Skill UI

A comprehensive guide for writing the 'Related Work' section for top-tier speech conference papers. It teaches authors how to trace the academic lineage of a task across multiple venues (e.g., ISCA, ICASSP, NeurIPS), properly cite canonical corpora and metrics, and condense complex literature reviews into concise, contrastive clauses.

This guide provides a sophisticated framework for structuring the related work section when submitting research to KDD. It instructs authors on how to position their work within the deep academic lineage of data mining, comparing their novelty not just to prior KDD volumes, but also to major ML flagships (ICML, NeurIPS) and specialized venues (WWW, ICDM). Learn to articulate mechanism-level differences and avoid common citation traps.

A comprehensive guide for authors to position their ML systems research by mapping the vast, scattered literature across multiple venues (OSDI, SOSP, NeurIPS, open-source projects). It details how to audit novelty, handle fast-moving advancements, and write a defensible delta statement against both academic and industrial prior work.

This guide helps researchers determine if their project genuinely involves machine learning and computer system co-design. It outlines criteria, such as the "co-design test," and provides a routing table to decide whether the work belongs at MLSys compared to general systems conferences (OSDI, SOSP) or pure ML venues (ICML, NeurIPS).

This skill provides a comprehensive framework for authors targeting top-tier AI conferences like NeurIPS. It guides researchers in refining their manuscript's narrative, ensuring the contribution meets the venue's standards for broad, high-novelty machine learning research. It covers topic fit, evidence bar requirements, structuring the argument, handling interdisciplinary claims, and navigating the specific submission cycle rules for maximizing acceptance chances.

Provides comprehensive guidelines for packaging and presenting research artifacts (code, data, models, benchmarks) submitted to NeurIPS. It details requirements for anonymous review packages, public release, and ensures scientific claims are reproducible by providing necessary metadata, licenses, and execution details.

A detailed guide for authors drafting responses, rebuttals, and discussions for major conferences like NeurIPS. It emphasizes adhering to strict double-blind constraints, focusing on technical evidence rather than arguing tone, and structuring responses to clarify misunderstandings and move reviewers toward an accurate decision.

This comprehensive guide outlines the necessary steps and artifacts required for submitting a camera-ready version of an accepted NeurIPS paper. It covers de-anonymization, updating metadata, resolving final content issues, and ensuring all public-facing elements meet official guidelines.

A comprehensive guide for stress-testing the evidence supporting ML claims before submitting to top-tier academic conferences. Covers rigorous auditing of baselines, ablation studies, robustness checks, data leakage, and calibrating claims against evidence strength, ensuring scientific reproducibility.

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.

A comprehensive guide for authors submitting to machine learning conferences like NeurIPS. It details the rigorous standards for data splitting, hyperparameter reporting, random-seed protocols, and compute transparency required for a main-track paper. It also helps authors strategically decide between the main track and the MLRC/TMLR routes based on their core contribution (replication vs. novelty).

This skill simulates the rigorous peer review process used in top-tier AI and machine learning conferences (like NeurIPS). It helps users diagnose potential weaknesses in a submission regarding quality, clarity, significance, ethical compliance, and reproducibility. It guides the identification of decision-relevant issues that ACs and reviewers are likely to raise, helping authors strategically plan responses for revision.