Youtor
Variational Autoencoders
>> YOUR LINK HERE: ___ http://youtube.com/watch?v=9zKuYvjFFS8
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: • / 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-steenbr...
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