arXiv Machine Learning
CDFlow: Building Invertible Layers with Circulant and Diagonal Matrices
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Normalizing flows are powerful generative models that learn an invertible mapping between complex data distributions and simple latent distributions, typically a standard normal density. However, this choice of latent density can impose unnecessary complexity on the learned flow transformation due to the topological mismatch between the latent and data densities, leading to slower training and suboptimal performance.
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Simplifying Flow Matching Transformations with Low-Rank Mixture Models
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arXiv Machine Learning
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Random Projection Flows for Efficient Manifold Density Estimation
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arXiv Machine Learning
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