A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …

Abstract Representation learning algorithms are designed to learn abstract features that
characterize data. State representation learning (SRL) focuses on a particular kind of …

Contemporary neural networks still fall short of human-level generalization, which extends
far beyond our direct experiences. In this paper, we argue that the underlying cause for this …

In this work, we perform unsupervised learning of representations by maximizing mutual
information between an input and the output of a deep neural network encoder. Importantly …

We decompose the evidence lower bound to show the existence of a term measuring the
total correlation between latent variables. We use this to motivate the beta-TCVAE (Total …

For an autonomous agent to fulfill a wide range of user-specified goals at test time, it must be
able to learn broadly applicable and general-purpose skill repertoires. Furthermore, to …

This work introduces a novel principle we call disentanglement via mechanism sparsity
regularization, which can be applied when the latent factors of interest depend sparsely on …

Decision-making is challenging in robotics environments with continuous object-centric
states, continuous actions, long horizons, and sparse feedback. Hierarchical approaches …

Disentangled representation learning has been proposed as an approach to learning
general representations even in the absence of, or with limited, supervision. A good general …

Deep CNNs are known to exhibit the following peculiarity: on the one hand they generalize
extremely well to a test set, while on the other hand they are extremely sensitive to so-called …