This episode breaks down the 'Variational Lossy Autoencoder' research paper, which proposes a novel deep learning model called the Variational Lossy Autoencoder (VLAE). The VLAE combines Variational Autoencoders (VAEs), which use latent variables to represent data, with autoregressive models, which model data sequentially. The authors analyse the information preference of VAEs and show that they can be used to learn lossy representations by carefully designing the decoding distribution. They introduce the concept of Bits-Back Coding, providing an information-theoretic perspective on VAE efficiency. The VLAE leverages autoregressive models both as the prior distribution over latent variables and as the decoding distribution, leading to improved density estimation performance and the ability to learn representations that capture global information. Experiments on various image datasets demonstrate the VLAE's ability to learn lossy codes and achieve state-of-the-art results on density estimation tasks.

Audio : (Spotify) https://open.spotify.com/episode/6MNMp6uaNFFMdo7NSGFX8c?si=JS7Wdy3JSwuyuzYw27eczQ

Paper: https://arxiv.org/pdf/1611.02731