Code, run specifications, and per-run artifacts for 'A Cross-Domain Empirical Benchmark of Quantum-Inspired and Classical Optimization Algorithms for Machine Learning'. The main-text result tables and generated figures of the paper regenerate programmatically from this tree (v2/codes/analysis/), with the labelled exceptions stated in the manuscript: the appendix tables retain v1 values verbatim, and in the QPSO cost table the LSTM Adam reference times are v1 measurements while the AG News row is a projection.
Code, run specifications, and per-run artifacts for 'A Cross-Domain Empirical Benchmark of Quantum-Inspired and Classical Optimization Algorithms for Machine Learning'. All main-text tables and figures of the paper regenerate programmatically from this tree (v2/codes/analysis/).
Code, run specifications, and per-run artifacts for 'A Cross-Domain Empirical Benchmark of Quantum-Inspired and Classical Optimization Algorithms for Machine Learning'. All main-text tables and figures of the paper regenerate programmatically from this tree (v2/codes/analysis/).
This repository archives the data and code accompanying the manuscript: "Spatial but not temporal predictability of Korean extreme-rainfall change: limits of covariate machine learning and a climate-factor implementation of the Clausius-Clapeyron / nonstationary-GEV alternative."
Initial public release This is the first public software release accompanying the manuscript: A Theory-Grounded Diagnostic Framework for Temporal Computation in Spiking Neural Networks The framework separates four properties that are often conflated in the analysis of spiking neu…
Reproduction data for the associated manuscript This record contains the software, input files, and precomputed data required to reproduce the calculations reported in Algorithmic first-principles reaction discovery uncovers overlooked cubane transformations The archive includes…
Open-source framework for monthly precipitation prediction in mountainous areas using hybrid deep learning. The framework provides reference implementations for eight model families and a uniform training, evaluation, and benchmarking pipeline: ConvLSTM family — baseline, bidirec…