The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.
Dynamic Matrix Inverse: Improved Algorithms and Matching Conditional Lower Bounds , booktitle =
4 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
roles
dataset 1polarities
use dataset 1representative citing papers
A classification of 179 MISRA C++ rules for Rust finds 47.75% of 111 applicable rules auto-enforced by the language, with 36 more satisfied automatically in safe Rust only.
STAF applies sentence embeddings from transformers to classify SCA findings, reaching 89% F1 and beating prior filters by 11% within projects and 6% across projects.
DeepFWI is a multi-modal LSTM model with cross-attention that identifies bug-sensitive warnings at warning granularity, reaching 67.06% F1 on a 280k-warning dataset and surfacing 25 confirmed bugs in four open-source projects.
citing papers explorer
-
Dynamic Rank, Basis, and Matching
The first dynamic algorithms for matrix rank and related objects achieve update times scaling with rank r, specifically Õ(r^1.405) per entry update and Õ(r^1.528 + z) per column update, extending to dynamic maximum matching.