Transformers have significantly impacted domains like natural language processing,
computer vision, and robotics, where they improve performance compared to other neural …

The recent success of transformer-based image generative models in object-centric learning
highlights the importance of powerful image generators for handling complex scenes …

The binding problem in human cognition, concerning how the brain represents and
connects objects within a fixed network of neural connections, remains a subject of intense …

Current object-centric learning models such as the popular SlotAttention architecture allow
for unsupervised visual scene decomposition. Our novel MusicSlots method adapts …

We present a framework for learning useful subgoals that support efficient long-term
planning to achieve novel goals. At the core of our framework is a collection of rational …

While modern best practices advocate for scalable architectures that support long-range
interactions, object-centric models are yet to fully embrace these architectures. In particular …

Learning disentangled representations of concepts and re-composing them in unseen ways
is crucial for generalizing to out-of-domain situations. However, the underlying properties of …

Visual reasoning is supported by a causal understanding of the physical world, and theories
of human cognition suppose that a necessary step to causal understanding is the discovery …

Current state-of-the-art synchrony-based models encode object bindings with complex-
valued activations and compute with real-valued weights in feedforward architectures. We …

In the presented study, we discover that the so-called" transition freedom" metric appears
superior for unsupervised tokenization purposes in comparison to statistical metrics such as …