Explore >> Select a destination

You are here

 www.depthfirstlearning.com
Variational Inference with Normalizing Flows ยท Depth First Learning
| | yang-song.net
Generative Modeling by Estimating Gradients of the Data Distribution | Yang Song
1.2 parsecs away

Travel
| | 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 ...
| | kyunghyuncho.me
Stochastic variational inference for low-rank stochastic block models, or how i re-discovered SBM unnecessarily - Kyunghyun Cho
2.4 parsecs away

Travel
| |
| | tiao.io
A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
0.8 parsecs away

Travel
| | An in-depth practical guide to variational encoders from a probabilistic perspective.
| | igorstechnoclub.com
Week 5 in Data Science: Image recognition neural network with 90% accuracy - a trivial task | Igor's Techno Club
3.9 parsecs away

Travel
| This week I learned something that finally made "transfer learning" click. I had always heard that you can hit strong accuracy fast by reusing a pretrain...