|
You are here |
almostsuremath.com | ||
| | | | |
nickhar.wordpress.com
|
|
| | | | | 1. Low-rank approximation of matrices Let $latex {A}&fg=000000$ be an arbitrary $latex {n \times m}&fg=000000$ matrix. We assume $latex {n \leq m}&fg=000000$. We consider the problem of approximating $latex {A}&fg=000000$ by a low-rank matrix. For example, we could seek to find a rank $latex {s}&fg=000000$ matrix $latex {B}&fg=000000$ minimizing $latex { \lVert A - B... | |
| | | | |
ggcarvalho.dev
|
|
| | | | | Using the power of randomness to answer scientific questions. | |
| | | | |
jxmo.io
|
|
| | | | | A primer on variational autoencoders (VAEs) culminating in a PyTorch implementation of a VAE with discrete latents. | |
| | | | |
solid-angle.blogspot.com
|
|
| | | Programmers don't generally have reels, but we do have blogs. I've been explaining the rendering work I did on BioShock Infinite quite a b... | ||