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Variational Autoencoders

In this episode, we dive into Variational Autoencoders, a class of neural networks that can learn to compress data completely unsupervised!

VAE's are a very hot topic right now in unsupervised modelling of latent variables and provide a unique solution to the curse of dimensionality.

This video starts with a quick intro into normal autoencoders and then goes into VAE's and disentangled beta-VAE's.
I aslo touch upon related topics like learning causal, latent representations, image segmentation and the reparameterization trick!

Get ready for a pretty technical episode!

Paper references:
- Disentangled VAE's (DeepMind 2016): https://arxiv.org/abs/1606.05579
- Applying disentangled VAE's to RL: DARLA (DeepMind 2017): https://arxiv.org/abs/1707.08475
- Original VAE paper (2013): https://arxiv.org/abs/1312.6114

If you want to support this channel, here is my patreon link:
https://patreon.com/ArxivInsights --- You are amazing!! ;)

If you have questions you would like to discuss with me personally, you can book a 1-on-1 video call through Pensight: https://pensight.com/x/xander-steenbrugge

Видео Variational Autoencoders канала Arxiv Insights
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Информация о видео
25 февраля 2018 г. 21:21:11
00:15:05
Яндекс.Метрика