Explore >> Select a destination
|
You are here 
|
akosiorek.github.io | ||
| | | | |
tiao.io
|
|
| | | | | An in-depth practical guide to variational encoders from a probabilistic perspective. | |
| | | | |
iclr-blogposts.github.io
|
|
| | | | | The transfer of matching-based training from Diffusion Models to Normalizing Flows allows to fit expressive continuous normalizing flows efficiently and therefore enables their usage for different kinds of density estimation tasks. One particularly interesting task is Simulation-Based Inference, where Flow Matching enabled several improvements. The post shall focus on the discussion of Flow Matching for Continuous Normalizing Flows. To highlight the relevance and the practicality of the method, their use and advantages for Simulation-Based Inference is elaborated. | |
| | | | |
blog.evjang.com
|
|
| | | | | This tutorial will show you how to use normalizing flows like MAF, IAF, and Real-NVP to deform an isotropic 2D Gaussian into a complex cl... | |
| | | | |
resources.paperdigest.org
|
|
| | | The Conference on Neural Information Processing Systems (NIPS) is one of the top machine learning conferences in the world. Paper Digest Team analyze all papers published on NIPS in the past years, and presents the 15 most influential papers for each year. This ranking list is automatically construc | ||
