Skill UI

Collection of battle-tested PyTorch training recipes covering architecture selection, optimizers, LR scheduling, precision tips, and debugging to train or fine-tune LLMs, vision, diffusion, and biomedical models with high GPU throughput.

This guide provides a rigorous strategy for authors responding to major or minor revisions in cognitive psychology journals. It teaches how to systematically address every reviewer comment, strengthen model-driven inferences through techniques like model comparison and recovery analysis, and maintain the coherence and reproducibility of the theoretical argument. Essential reading for scholarly publication.

A comprehensive project management guide for submitting database systems papers to EDBT. It details the complexities of the multiple-cycle rolling publication model, covering everything from initial evidence lock and cycle selection to the five-step review arc (Submission, Author Feedback, Revision, etc.). Use this to backward-plan your research timeline and manage the entire academic publishing process.

A comprehensive guide modeling the specific, single-round, double-anonymous review pipeline for IEEE ICSME submissions. It details the crucial early-decision cut (Accept, Reject, Response Recommended) and emphasizes that unlike journal submissions, there are no Major Revision rounds. Use this to strategically plan your research submission and response artifact.

This guide provides a detailed model of the PerCom conference review process, covering double-blind review, the three-TPC-member evaluation model, the early-rejection gate, and the bounded rebuttal stage. It clarifies how authors should structure their submissions to succeed in this single-round, non-revisionary academic environment, differentiating it sharply from journal cycles.

This guide details how to structure a scholarly response document after receiving major revisions for a theoretical model. It emphasizes that the response must argue from substantive theoretical improvements—by adding derivations, simulations, or clarifying scope—rather than merely defending the original wording. It provides a rigorous, point-by-point framework for authors addressing critical academic feedback.

A comprehensive guide for tackling a 'revise-and-resubmit' academic review (R&R). This process details how to substantively resolve critical, first-order concerns—such as identification threats, construct measurement, or model assumptions—before drafting the letter. It emphasizes rigorous analysis, structured point-by-point responses, maintaining manuscript consistency, and preserving anonymity for resubmission.