Explore >> Select a destination
|
You are here 
|
almostsuremath.com | ||
| | | | |
jxmo.io
|
|
| | | | | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents. | |
| | | | |
gregorygundersen.com
|
|
| | | | | [AI summary] The post discusses the simulation of geometric Brownian motion (GBM) using Python, explaining its mathematical foundations and verifying results against theoretical models. | |
| | | | |
yang-song.net
|
|
| | | | | This blog post focuses on a promising new direction for generative modeling. We can learn score functions (gradients of log probability density functions) on a large number of noise-perturbed data distributions, then generate samples with Langevin-type sampling. The resulting generative models, often called score-based generative models, has several important advantages over existing model families: GAN-level sample quality without adversarial training, flexible model architectures, exact log-likelihood ... | |
| | | | |
www.jerpint.io
|
|
| | | A collection of anything and everything. | ||
