Continuous Normalizing Flows
PulseAugur coverage of Continuous Normalizing Flows — every cluster mentioning Continuous Normalizing Flows across labs, papers, and developer communities, ranked by signal.
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New infinite-dimensional normalizing flow model for Bayesian inverse problems
Researchers have developed a novel infinite-dimensional continuous normalizing flow model to address Bayesian inference for inverse problems involving partial differential equations. This model utilizes a neural ordinar…
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New Hierarchical Flow Matching method generates 3D point clouds
Researchers have introduced Hierarchical Flow Matching (HFM), a novel method for generating 3D point clouds. HFM addresses limitations in existing flow-based and diffusion models by employing a two-level approach that c…
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New PMOT Framework Uses Continuous Normalizing Flows for Optimal Transport
Researchers have introduced Potential Matching Optimal Transport (PMOT), a novel framework utilizing continuous normalizing flows to address general $p$-cost optimal transport problems. PMOT parameterizes the flow's vel…
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New library unifies 12 generative models as mean-field games
Researchers have introduced MFGLab, an open-source PyTorch library that unifies twelve continuous-time generative models under a single variational problem framework. This approach treats models like Continuous Normaliz…
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New Boltzmann generators tackle amorphous materials in statistical physics
Researchers have developed a new type of Boltzmann generator specifically designed for amorphous materials, which are notoriously difficult to sample equilibrium states from due to their disordered structure. This novel…
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New papers unify generative flows and use Koopman operators
Two new research papers explore advanced techniques in generative modeling. The first paper introduces Generative Wasserstein Flows (GWF) as a unified framework for various generative models, extending to new algorithms…