Symmetry-based neural networks often constrain the architecture in order to achieve
invariance or equivariance to a group of transformations. In this paper, we propose an …

Supervised learning with deep models has tremendous potential for applications in
materials science. Recently, graph neural networks have been used in this context, drawing …

Set function learning has emerged as a crucial area in machine learning, addressing the
challenge of modeling functions that take sets as inputs. Unlike traditional machine learning …

Causal representation learning has showed a variety of settings in which we can
disentangle latent variables with identifiability guarantees (up to some reasonable …

Deep Equilibrium (DEQ) Models, an emerging class of implicit models that maps inputs to
fixed points of neural networks, are of growing interest in the deep learning community …

Systems of competing agents can often be modeled as games. Assuming rationality, the
most likely outcomes are given by an equilibrium, eg, a Nash equilibrium. In many practical …

Using symmetry as an inductive bias in deep learning has been proven to be a principled
approach for sample-efficient model design. However, the relationship between symmetry …

Slot attention is a powerful method for object-centric modeling in images and videos.
However, its set-equivariance limits its ability to handle videos with a dynamic number of …