Introduces the Insertion Process model for variable-length non-monotonic sequence generation via a bijective permutation mapping and permutation-based variational inference.
Malliaros, and Christopher Morris
4 Pith papers cite this work. Polarity classification is still indexing.
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IDDM interpolates diffusion transitions with a resampling mechanism to lessen dependence on intermediate latents and improve sample quality over masked and uniform discrete diffusion models.
EQUIMF is a unified equivariant framework that jointly generates discrete topologies and continuous geometries in molecular graphs via synchronized MeanFlow dynamics for efficient few-step sampling.
GraphBSI uses Bayesian Sample Inference as noise-controlled SDEs to generate discrete graphs in one shot, achieving state-of-the-art results on molecular benchmarks Moses and GuacaMol.
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Discrete Bayesian Sample Inference for Graph Generation
GraphBSI uses Bayesian Sample Inference as noise-controlled SDEs to generate discrete graphs in one shot, achieving state-of-the-art results on molecular benchmarks Moses and GuacaMol.