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learning algorithm » learning algorithms (Expand Search)
elements learning » students learning (Expand Search), elements during (Expand Search)
coding algorithm » cosine algorithm (Expand Search), modeling algorithm (Expand Search), finding algorithm (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
level coding » level according (Expand Search), level modeling (Expand Search), level using (Expand Search)
learning algorithm » learning algorithms (Expand Search)
elements learning » students learning (Expand Search), elements during (Expand Search)
coding algorithm » cosine algorithm (Expand Search), modeling algorithm (Expand Search), finding algorithm (Expand Search)
data algorithm » data algorithms (Expand Search), update algorithm (Expand Search), atlas algorithm (Expand Search)
element data » settlement data (Expand Search), relevant data (Expand Search), movement data (Expand Search)
level coding » level according (Expand Search), level modeling (Expand Search), level using (Expand Search)
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Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone
Published 2024“…We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithmsk-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. …”
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The run time for each algorithm in seconds.
Published 2025“…The goal of this paper is to examine several extensions to KGR/GPoG, with the aim of generalising them a wider variety of data scenarios. The first extension we consider is the case of graph signals that have only been partially recorded, meaning a subset of their elements is missing at observation time. …”
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RealBench: A Repo-Level Code Generation Benchmark Aligned with Real-World Software Development Practices
Published 2025“…<br>│ └── level4/ # Level 4 projects (expert complexity).<br>├── src/ # Main source code for the RealBench framework.…”
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