Skill UI

A comprehensive audit guide for submitting academic papers to the FAccT conference. It details critical, multi-step processes, including the mandatory abstract registration gate, maintaining mutual anonymity, adhering to strict ACM formatting rules, and including required endmatter statements (such as the Generative AI Usage Statement).

This guide helps researchers determine the most appropriate academic venue for their responsible AI work. It provides detailed criteria and rigorous testing methods (like the Delete-the-equity test) to assess whether fairness, accountability, or transparency is a primary, core contribution, guiding authors away from merely 'bolted-on' ethical framing.

A comprehensive project management guide for submitting interdisciplinary, responsible AI papers to major academic conferences like FAccT. It provides a detailed backward-planning timeline, covering milestones from initial abstract registration and literature review to revisions, camera-ready submission, and conference presentation, emphasizing ethical considerations and structured academic processes.

Genomic Intelligence provides powerful, hosted transformer models for advanced genomic analysis. Users can input a DNA/FASTA sequence and receive structured predictions for key regulatory features, including promoter regions, splice sites, enhancer activity, chromatin state, gene expression (log TPM), and de-novo gene annotation. This service operates via a REST API or MCP server, eliminating the need for local GPU hardware or model weights, making complex bioinformatics accessible through sequence input.

This comprehensive guide helps authors navigate the complex decision of where to submit their Human-Robot Interaction (HRI) research. It outlines critical criteria, including the robot-embodiment and human-in-the-loop tests, to determine the correct venue (e.g., HRI, CHI, ICRA/IROS). Furthermore, it details the five specialized tracks within HRI and advises authors on matching their primary technical contribution to the most suitable academic domain.

A comprehensive guide detailing the critical workflow from paper acceptance to final camera-ready submission for major computational conferences (like ICCV). It covers technical requirements such as de-anonymization, synchronizing multiple public copies (arXiv, CVF, IEEE), updating URLs, and managing logistical details for presentation.

A methodology guide for structuring high-stakes academic research experiments, particularly for top-tier CV conferences (like ICCV). It emphasizes systematic planning under strict deadlines, covering benchmark drift audits, ensuring fairness by accounting for pretraining confounds, structuring ablations to isolate mechanisms, and strategically sequencing runs to maximize impact before the submission deadline.

This comprehensive guide serves as a pre-flight audit checklist for major academic conference submissions, specifically targeting ICCV. It covers critical process areas, including the single-day upload requirement, strict page budget adherence, maintaining double-blind anonymity, vetting OpenReview fields, and identifying potential identity or formatting leaks. Use this to prevent desk rejection before the deadline.

A comprehensive guide for researchers managing the complex, year-long cycle of major computer vision conferences (like ICCV). Covers critical stages from the initial 'autumn fork' decision and submission deadlines to rebuttal, final decisions, and post-conference strategies, including international travel logistics and fallback plans.

This comprehensive guide outlines the critical steps for authors preparing their accepted ICLR paper for camera-ready submission. It covers de-anonymization procedures, incorporating all fixes promised during the discussion period, completing OpenReview metadata, and assembling all necessary presentation assets, including posters, slide decks, and videos, according to current conference guidelines.

This guide provides comprehensive best practices for designing and auditing empirical experiments for top-tier ML conferences. It covers necessary ablations, robustness checks (e.g., data shift, seeds), fair baselines, and detailed compute reporting to ensure that reported gains are scientifically genuine, reproducible, and address common reviewer skepticism.

A comprehensive guide for academic authors detailing how to establish and maintain rigorous reproducibility in Machine Learning research papers (e.g., ICLR). It instructs on mapping every central claim to verifiable evidence, including recording seeds, variance, specific compute budgets (training vs. inference), data preprocessing scripts, and ethical considerations. The statement must function as a public contract for the scientific community.