Deep neural networks excel in supervised learning tasks but are constrained by the need for
extensive labeled data. Self-supervised learning emerges as a promising alternative …

The availability of large datasets has boosted the application of machine learning in many
fields and is now starting to shape active-matter research as well. Machine learning …

Human observers can learn to recognize new categories of images from a handful of
examples, yet doing so with artificial ones remains an open challenge. We hypothesize that …

We present a variational approximation to the information bottleneck of Tishby et al.(1999).
This variational approach allows us to parameterize the information bottleneck model using …

State representation learning, or the ability to capture latent generative factors of an
environment is crucial for building intelligent agents that can perform a wide variety of tasks …

Models of working memory (WM) commonly focus on how information is encoded into and
retrieved from storage at specific moments. However, in the majority of real-life processes …

We derive a principled information-theoretic account of cross-language semantic variation.
Specifically, we argue that languages efficiently compress ideas into words by optimizing the …

Humans learn powerful representations of objects and scenes by observing how they evolve
over time. Yet, outside of specific tasks that require explicit temporal understanding, static …

“Neural coding” is a popular metaphor in neuroscience, where objective properties of the
world are communicated to the brain in the form of spikes. Here I argue that this metaphor is …

Most models of sensory processing in the brain have a feedforward architecture in which
each stage comprises simple linear filtering operations and nonlinearities. Models of this …