framework for extracting compact latent representations to encode predictions over future observations
learn such representations by predicting the future in latent space by using powerful Autoregressive models
probabilistic Contrastive Loss based on NCE, which both the encoder and Autoregressive model are trained to jointly optimize, which they call InfoNCE
InfoNCE induces the latent space to capture information that is maximally useful to predict future samples
combines Autoregressive modeling and noise-contrastive estimation with intuitions from predictive coding to learn abstract representations in an unsupervised fashion