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Found 5 papers, 4 papers with code
Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos.
RINGER: Rapid Conformer Generation for Macrocycles with Sequence-Conditioned Internal Coordinate Diffusion
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformational diversity and geometric constraints.
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion
Our framework for graph diffusion can have a large impact on the interpretable conditional generation of graphs, including the generation of drug-like molecules with desired properties in a way which is informed by experimental evidence.
Improving Graph Generation by Restricting Graph Bandwidth
However, one of the main limitations of existing methods is their large output space, which limits generation scalability and hinders accurate modeling of the underlying distribution.
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
We highlight several advantages of branched diffusion models over the current state-of-the-art methods for class-conditional diffusion, including extension to novel classes in a continual-learning setting, a more sophisticated form of analogy-based conditional generation (i. e. transmutation), and a novel interpretability into the generation process.
