Branching Flows extends flow matching to variable-sized states by letting elements branch and delete stochastically on binary trees while composing with discrete, continuous, manifold, or multimodal base processes.
Trans-dimensional generative modeling via jump diffusion models
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
Morph is a flexible-size 3D molecular generative model using unbalanced optimal transport on geometric graphs that matches fixed-size SOTA performance while enabling out-of-distribution 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.
citing papers explorer
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Branching Flows: Discrete, Continuous, and Manifold Flow Matching with Splits and Deletions
Branching Flows extends flow matching to variable-sized states by letting elements branch and delete stochastically on binary trees while composing with discrete, continuous, manifold, or multimodal base processes.
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Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport
Morph is a flexible-size 3D molecular generative model using unbalanced optimal transport on geometric graphs that matches fixed-size SOTA performance while enabling out-of-distribution generation.
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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.