An author's paper was rejected from a workshop for sounding more like a technical report than a research paper, despite following the usual computer vision format. They are seeking community opinion to understand common faux pas that lead to such an assessment.
The user queries whether there's increased pressure on Area Chairs (ACs) at ICML to ensure reviewers provide final justifications and reach consensus. They note a disparity, observing active AC engagement and final justifications for papers they reviewed, but complete silence and missing justifications for their own submission despite showing some disagreement in scores.
The user is considering shifting from ML conferences to journals due to negative reviewing experiences, seeking advice on alternatives to JMLR and TMLR. They are curious about journals like Neurocomputing and Neural Networks, specifically regarding their selectivity and quality despite their Q1 status in the conference-oriented ML world.
A user is seeking guidance on how to anonymize code repositories for AI/ML conference submissions. They specifically ask if creating a separate anonymous GitHub account is common practice.
A researcher asks for feedback on whether to stop sharing code in ML conference submissions (e.g., NIPS, ICML) due to concerns about idea theft, suggesting publishing it only after acceptance. They note that while reviewers often expect code, some recent submissions without it haven't been penalized, and other reproducibility aspects could be emphasized.
A user observes that arXiv submission "on-hold" durations have significantly increased from a couple of days to about two weeks. They speculate this is due to an inundation of low-effort AI-generated papers and ask if others are facing the same issue.
AI research papers are getting better, which poses a significant problem for scientists. A 2017 paper on statistical analysis of epidemiological data received an unusually high number of recent citations, prompting a professor to investigate the validity of these references.
ArXiv will ban researchers for a year if their papers contain "incontrovertible evidence" of unchecked LLM generation, such as hallucinated references. Future submissions from these authors will also require acceptance from a reputable peer-reviewed venue.
This paper proposes a two-layer certification framework for publications, separating knowledge quality assessment from human contribution in AI-enabled research. It allows publication systems to consistently handle pipeline-generated work, categorizing contributions as pipeline-reachable, human-directed, or beyond human reach.
This paper argues against using current AI systems for peer review, identifying two critical issues: a "hivemind effect" that reduces perspective diversity and the trivial gameability of AI review scores through paper rewriting. Empirical comparison of human- versus AI-generated reviews shows that AI reviewers are susceptible to stylistic changes rather than scientific merit, highlighting the need for non-gameability and review diversity for automation.
Um usuário enfrentou um erro de 'Corrupt PDF: Parser error' ao validar um manuscrito para submissão no CVPR usando o IEEE PDF eXpress, apesar de seguir as regras de formatação, e está buscando ajuda.
A user raises concerns about an alleged influential Chinese network in AI conferences (like IJCAI) practicing nepotism and rejecting papers from non-Chinese authors. The author shares a personal experience where a reviewer demanded a citation to a paper by a Chinese lead author.
The title suggests a discussion about how the 'conference lottery culture' might be detrimental to research quality. It addresses concerns that the selection process at conferences negatively impacts scientific progress.