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Found 6 papers, 5 papers with code
Learning Distributions on Manifolds with Free-form Flows
Many real world data, particularly in the natural sciences and computer vision, lie on known Riemannian manifolds such as spheres, tori or the group of rotation matrices.
Free-form Flows: Make Any Architecture a Normalizing Flow
Normalizing Flows are generative models that directly maximize the likelihood.
On the Convergence Rate of Gaussianization with Random Rotations
Gaussianization is a simple generative model that can be trained without backpropagation.
Lifting Architectural Constraints of Injective Flows
Normalizing Flows explicitly maximize a full-dimensional likelihood on the training data.
Generative Invertible Quantum Neural Networks
Invertible Neural Networks (INN) have become established tools for the simulation and generation of highly complex data.
Generative Networks for Precision Enthusiasts
Generative networks are opening new avenues in fast event generation for the LHC.
