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ACL Writing Style for Computational Linguistics

acl-writing-style

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for revising NLP and Computational Linguistics manuscripts for submission to top-tier conferences like ACL. It mandates adopting a task-first, evidence-grounded approach, strictly scoping claims in the LLM era, and ensuring linguistic rigor in all examples and analyses. Learn how to structure, compress, and refine your arguments for maximum impact.

Intelligent User Interfaces Conference Guide

acm-conference-on-intelligent-user-interfaces

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for authors targeting the ACM Conference on Intelligent User Interfaces (IUI). This skill helps evaluate manuscript fit, refine scope, understand evidence requirements, and strategically position papers related to AI-infused interaction, adaptive systems, and user modeling for top-tier HCI venues.

ACM Multimedia Venue Fit Guide

acm-international-conference-on-multimedia

brycewang-stanford/Awesome-Journal-Skills

This guide helps authors assess the suitability of their computer science manuscripts for the ACM International Conference on Multimedia (ACM MM). It provides detailed strategies for framing, highlighting multimodal contributions, structuring evidence, and aligning the work with top-tier academic conference expectations in multimedia retrieval, multimodal learning, and media applications.

Ensuring Multimedia Research Reproducibility

acmmm-reproducibility

brycewang-stanford/Awesome-Journal-Skills

A guide for researchers submitting to conferences like ACM Multimedia, detailing how to achieve robust result reproducibility. It covers the necessary steps—including logging code commits, data versions, media preprocessing parameters, and hardware details—to allow independent reviewers to successfully rebuild the reported findings and achieve official reproducibility badges.

Guide To ACM Multimedia Topic Selection

acmmm-topic-selection

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for researchers to determine if their work represents a genuine cross-modal or media-systems contribution suitable for ACM Multimedia (ACM MM). It helps route projects by testing for cross-modality, media system advances, or human-centric results, distinguishing between pure CV, NLP, or system-heavy claims.

Model Team Orchestration Workflow

advisor-orchestrator-worker

Shubhamsaboo/awesome-llm-apps

This system acts as an orchestrator for a three-tier model team (Advisor, Workers). It is designed for complex, large-scale tasks that exceed the capability of a single model pass, requiring decomposition, parallelization, and mandatory verification loops. Use cases include researching multiple competitors, executing multi-stage plans, or managing iterative development cycles involving different specialized AI agents.

AISTATS Conference Submission Strategy Guide

artificial-intelligence-and-statistics

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for authors preparing manuscripts for the International Conference on Artificial Intelligence and Statistics (AISTATS). It advises on whether the paper's contribution is statistically grounded and theory-backed, guiding framing, evidence presentation, and understanding reviewer expectations in statistical machine learning.

British Machine Vision Conference Pitch Guide

british-machine-vision-conference

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for authors targeting the British Machine Vision Conference (BMVC). This skill helps assess whether a computer vision manuscript fits the venue, providing expert advice on framing, necessary evidence gaps, structural improvements, and differentiating the work from related CV conferences (e.g., CVPR, ICCV). It ensures the contribution is clear and highly specialized for specialist reviewers.

CIKM Submission Readiness Checklist

cikm-submission

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing the mandatory submission requirements for major AI conferences like CIKM. It covers critical areas such as strict page budget rules (including appendices), mandatory GenAI Usage Disclosure, EasyChair nomination processes, maintaining double-blind anonymity, and adhering to crucial deadlines like the abstract-gate week to prevent desk rejection.

Packaging AI Research Artifacts for Review

colm-artifact-evaluation

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide for authors on best practices for packaging, documenting, and ensuring the reproducibility of LLM artifacts (weights, data, code, prompts, etc.). It addresses requirements for anonymous academic review and public post-acceptance release, focusing on reproducibility, licensing, and transparency.

LLM Evaluation Rigor and Reproducibility

colm-experiments

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing best practices for designing and auditing empirical evaluations of Large Language Models (LLMs). It addresses critical pitfalls such as data contamination, baseline unfairness, model version drift, and decoding parameter sensitivity, ensuring that published results are robust, reproducible, and scientifically sound for academic publication.

Mastering The COLM Review Process Flow

colm-review-process

brycewang-stanford/Awesome-Journal-Skills

A comprehensive guide detailing the academic review pipeline for the COLM conference. It covers the entire journey from abstract submission to final decisions, emphasizing unique elements such as LLM use rules, reciprocal reviewing obligations, and strategic methods for maximizing impact during the rebuttal phase. Essential reading for authors planning submissions to specialized, young venues.

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