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Implementing NF and Maximum Likelihood Training on Gaussian and half-moon data

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NormalizingFlows

TUM Summer Term 2022

Machine Learning for Graphs and Sequential Data

Project 1 - Normalizing Flows Implementation of Affine and Radial Flow.

Maximum Likelihood Training on:

  • (1) 1 shifted (non-zero mean) Gaussian data,
  • (2) 3 Gaussians with mean on a circle,
  • (3) half-moon data.

p.s. The content is only for demonstration purposes since the data / some utility scripts are not included in the repo.

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Implementing NF and Maximum Likelihood Training on Gaussian and half-moon data

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