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Addressing Class Imbalance in Classification Problems of Noisy Signals by using Fourier Transform Surrogates
To quantify how well the distributions of the surrogates and the original data match, we evaluated a trained classifier on surrogates of correctly classified examples, and summarized these conditional predictions in a confusion matrix.
Understanding Machine-learned Density Functionals
Kernel ridge regression is used to approximate the kinetic energy of non-interacting fermions in a one-dimensional box as a functional of their density.
Orbital-free Bond Breaking via Machine Learning
Machine learning is used to approximate the kinetic energy of one dimensional diatomics as a functional of the electron density.
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