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Found 4 papers, 2 papers with code
DiPietro-Hazari Kappa: A Novel Metric for Assessing Labeling Quality via Annotation
Data is a key component of modern machine learning, but statistics for assessing data label quality remain sparse in literature.
Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems
Here we present Symplectically Integrated Symbolic Regression (SISR), a novel technique for learning physical governing equations from data.
Quantitative Stopword Generation for Sentiment Analysis via Recursive and Iterative Deletion
Empirically, stopword lists generated via this approach drastically reduce dataset size while negligibly impacting model performance, in one such example shrinking the corpus by 28. 4% while improving the accuracy of a trained logistic regression model by 0. 25%.
Sparse Symplectically Integrated Neural Networks
We introduce Sparse Symplectically Integrated Neural Networks (SSINNs), a novel model for learning Hamiltonian dynamical systems from data.
