[IJCAI 2025] Optimized View and Geometry Distillation from Multi-view Diffuser
-
Updated
May 2, 2025 - Jupyter Notebook
[IJCAI 2025] Optimized View and Geometry Distillation from Multi-view Diffuser
Memory efficient seismic inversion via trace estimation
A type theory for unbiased cartesian closed categories.
A dataset bucket with a machine learning bias auditor. Built with Python-Flask, MaterializeCSS and the Kaggle API.
Memory efficient convolution networks
Calculate the standard deviation of a strided array using Welford's algorithm.
Calculate the standard deviation of a strided array.
Calculate the variance of a strided array ignoring NaN values and using Welford's algorithm.
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.
Calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.
Calculate the standard deviation of a double-precision floating-point strided array using a one-pass textbook algorithm.
Compute a moving unbiased sample variance incrementally.
Calculate the variance of a single-precision floating-point strided array.
Compute a variance-to-mean ratio (VMR) incrementally.
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 standard deviation of a single-precision floating-point strided array.
Compute a moving sample absolute Pearson product-moment correlation coefficient incrementally.
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."