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Found 4 papers, 1 papers with code
Provable Compositional Generalization for Object-Centric Learning
Learning representations that generalize to novel compositions of known concepts is crucial for bridging the gap between human and machine perception.
Provably Learning Object-Centric Representations
Under this generative process, we prove that the ground-truth object representations can be identified by an invertible and compositional inference model, even in the presence of dependencies between objects.
Embrace the Gap: VAEs Perform Independent Mechanism Analysis
Leveraging self-consistency, we show that the ELBO converges to a regularized log-likelihood.
Normalizing flows for microscopic many-body calculations: an application to the nuclear equation of state
Normalizing flows are a class of machine learning models used to construct a complex distribution through a bijective mapping of a simple base distribution.
Nuclear Theory
