In this work, we introduce a new approach to model group actions in autoencoders.
Diverging from prior research in this domain, we propose to learn the group actions on the …

Abstract The Kohn-Sham equations underlie many important applications such as the
discovery of new catalysts. Recent machine learning work on catalyst modeling has focused …

The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling
parameter spaces, offering a unified strategy for handling continuous, discretized, and …

Many real-world decision-making problems are combinatorial in nature, where states (eg,
surrounding traffic of a self-driving car) can be seen as a combination of basic elements (eg …

Recent symmetry-based methods on variational autoencoders have advanced
disentanglement learning and combinatorial generalization, yet the appropriate symmetry …

Symmetries on representations within generative models have shown essential roles in
predicting unobserved combinations of semantic changes, known as combinatorial …