(IJCAI 2025) Optimized View and Geometry Distillation from Multi-view Diffuser
Memory efficient seismic inversion via trace estimation
A dataset bucket with a machine learning bias auditor. Built with Python-Flask, MaterializeCSS and the Kaggle API.
Memory efficient convolution networks
A type theory for unbiased cartesian closed categories.
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.
Calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.
Compute a moving unbiased sample variance incrementally.
Calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm.
Calculate the standard deviation of a strided array.
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.
Compute a sample Pearson product-moment correlation matrix incrementally.
Calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a one-pass trial mean algorithm.
Calculate the variance of a single-precision floating-point strided array using extended accumulation and returning an extended precision result.
Compute an arithmetic mean and unbiased sample variance incrementally.
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