Skill UI

A comprehensive guide for authors preparing rebuttals to multiple reviewers at INTERSPEECH. It provides strategic advice on tailoring responses to diverse expert profiles (e.g., speech engineers vs. scientists), managing the 4-page constraint, maintaining anonymity, and structuring responses to maximize impact on the meta-reviewer. Essential for authors navigating the competitive academic publication process.

A comprehensive guide for authors preparing camera-ready papers for the INTERSPEECH conference. It covers critical steps like de-anonymizing content, ensuring accurate metadata linking (DOI/ISCA Archive), meeting author registration requirements for proceedings inclusion, and planning oral or poster presentations.

This comprehensive guide outlines best practices for designing, evaluating, and reporting experimental results in speech processing research (ASR, TTS, Speaker Verification, etc.). It details required metrics (WER, MOS, EER), establishing robust baselines, conducting significance testing (bootstrap, CI), and ensuring comprehensive condition coverage (speakers, noise, languages) to enhance the scientific rigor of academic submissions.

This comprehensive guide outlines the strict technical and structural requirements for the IROS supplementary video, which serves as primary evidence for submitted research. It details constraints such as the 60-second limit, 10MB size cap, and necessary formats. The guide emphasizes presenting uncut real trials, including labeled failures, maintaining honest playback speed, and performing thorough anonymization to maximize academic credibility.

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 skill guides authors on positioning and framing manuscripts for the Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING). It provides detailed advice on core fit, required evidence (e.g., documentation, licensing, annotation reliability), and rhetorical structure needed to ensure a paper resonates with the specialized audience of language resources and computational linguistics.

This guide provides expert advice on positioning, framing, and validating medical imaging AI manuscripts for the MICCAI conference. It helps authors tailor their research to meet the venue's specific requirements, focusing on clinical-task grounding, robust validation, and alignment with core scopes like computational anatomy and image-guided intervention. Essential for optimizing conference submission strategy.

This comprehensive guide instructs researchers on how to design rigorous and credible evaluations for Machine Learning systems. It covers critical areas such as workload selection, baseline comparison discipline, reporting four key coordinates (throughput, latency tails, memory, cost), structuring ablations, and generating scaling evidence. It ensures measurements meet academic benchmarking standards.

A comprehensive guide for researchers detailing how to establish rigorous provenance for on-device performance measurements (e.g., energy, latency, accuracy). It mandates recording all necessary context—including device model, SoC, OS build, framework versions, power instrument setups, and thermal conditions—to ensure results are verifiable and reproducible across different hardware or environments for academic publication.

This skill serves as a detailed guide for authors submitting theoretical articles in syntax or morphology to Natural Language & Linguistic Theory (NLLT). It outlines the journal's specific expectations, emphasizing that formal analyses must be deeply grounded and rigorously tested using rich, representative, and often cross-linguistic empirical data. Use this tool to assess the methodological rigor, scope fit, and structural requirements of your manuscript against NLLT's high academic standards.

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.