Representational Learning Reading Group

Schedule: please find the list of the past and future papers here.

Contact: Balázs Ujfalussy (

General info: The reading group discusses (mostly recent) papers about machine learning models and algorithms that aim to learn structured, compressed and/or interpretable latent representations of observations, often with implications not only for ML problems such as machine vision or natural language processing, but also neuroscience and cognitive science. A brief introduction to the themes of the group and the interests of some founding members are described in this note.
The group is open for anyone, but operates under the assumption that participants know the basic tenets of unsupervised learning and probability theory and have read the paper assigned for the meeting. The list of previously discussed papers with notes (where recorded) together with the proposed papers for future meetings is here.