Memory efficient seismic inversion via trace estimation
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Updated
Jun 27, 2024 - Julia
Memory efficient seismic inversion via trace estimation
Memory efficient convolution networks
A dataset bucket with a machine learning bias auditor. Built with Python-Flask, MaterializeCSS and the Kaggle API.
Calculate the standard deviation of a strided array using Welford's algorithm.
Calculate the variance of a strided array ignoring NaN values and using Welford's algorithm.
Compute a sample Pearson product-moment correlation matrix incrementally.
Compute a variance-to-mean ratio (VMR) incrementally.
Calculate the variance of a strided array using a one-pass textbook algorithm.
Calculate the variance of a single-precision floating-point strided array.
Calculate the mean and variance of a double-precision floating-point strided array.
Calculate the mean and variance of a double-precision floating-point strided array using a two-pass algorithm.
Calculate the variance of a strided array.
Calculate the standard deviation of a strided array ignoring NaN values and using Welford's algorithm.
Calculate the standard deviation of a strided array ignoring NaN values and using a one-pass textbook algorithm.
Calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.
Calculate the variance of a strided array ignoring NaN values and using a one-pass algorithm proposed by Youngs and Cramer.
Calculate the variance of a double-precision floating-point strided array ignoring NaN values and using Welford's algorithm.
Compute an unbiased sample covariance incrementally.
Calculate the variance of a single-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
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