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cc2dataset

Easily convert common crawl to a dataset of caption and document. Image/text Audio/text Video/text, ...

easy

Google Hash Code Paris 2014

fcgan

Fast column generation for atomic norm regularization

learn-lvggm

Gaussian graphical models (GGM) have been widely used in many high-dimensional applications. Problems in which all relevant variables have been identified is extremely rare. The unobserved variables (or latent variables) can induce misleading dependence structure between observed variables. In this paper, we focus on a family of Latent Variable Gaussian graphical models (LVGGM), where the model is conditionally sparse given latent variables, but marginally non-sparse. In order to reveal latent variables and recover the structure of the complete model, we introduce a convex formulation with a new regularization imposing sparsity on latent factors. We propose a tractable convex algorithm and study identifiability conditions. We show promising results on synthetic datasets.

learning-gm

Estimation of Gaussian graphical models

magvit2-pytorch

Implementation of MagViT2 Tokenizer in Pytorch

nanogpt-for-image

Adaptation of nanoGPT for images

video2dataset

Easily create large video dataset from video urls

vinyesm.github.io