Memory efficient seismic inversion via trace estimation
-
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 single-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 mean and variance of a double-precision floating-point strided array.
Calculate the variance of a strided array 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.
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 standard deviation of a single-precision floating-point strided array using a two-pass algorithm.
Calculate the mean and standard deviation of a double-precision floating-point strided array.
Compute a sample absolute Pearson product-moment correlation coefficient.
Calculate the variance of a single-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.
Add a description, image, and links to the unbiased topic page so that developers can more easily learn about it.
To associate your repository with the unbiased topic, visit your repo's landing page and select "manage topics."