Speaker
Dr
Danilo Rezende
(Google Deep Mind)
Description
This presentation focuses on unsupervised representation learning. We first introduce the concept of representation learning, contrasting it with supervised learning. We then discuss several approaches to unsupervised representation learning, including those based on autoencoders, discriminators, contrastive and generative methods. Next, we shift our focus to generative models, discussing their foundations and different types of generative models. We highlight the relationship between unsupervised learning and the statistical, causal, and physical levels of modeling. Finally, we discuss the utility of unsupervised learning in a variety of downstream tasks, including image recognition, object detection, and reinforcement learning.
