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[P] Conditional Density Estimation Python Package and Large Benchmark with Neural Networks (MDN, KMN, Normalizing Flow) and Non-/Semi-parametric estimators

We’ve implemented an extensive pip package for Conditional Density Estimation that, among other features, includes Mixture Density Network, Kernel Mixture Network, Normalizing Flow Estimator and various non-parametric/semi-parametric estimators (CKDE, NKDE, LSKDE), data simulators and evaluation functions (centered moments, KL/JS divergence, Hellinger distance, percentiles etc.).

The package is constantly improved and we also provide a benchmark & best practices report and a code documentation.

Code: https://github.com/freelunchtheorem/Conditional_Density_Estimation

Benchmark and best practices paper for NN-based CDE: https://arxiv.org/abs/1903.00954

Code docs: https://freelunchtheorem.github.io/Conditional_Density_Estimation/docs/html/index.html

We’re open for suggestions and feedback so please feel free to use & comment. Lastly, if you like our project, we’d be happy if you spread the word and star the GH repo.

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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.