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[R] Noise Regularization for Conditional Density Estimation

In neural network-based conditional density estimation (CDE), classic regularization approaches in the parameter space are mostly ineffective. To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training. We demonstrate that the proposed approach corresponds to a smoothness regularization, we prove its asymptotic consistency and show across 7 datasets and 3 CDE models that this works well. Result: makes neural network-based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce!

Paper: https://arxiv.org/abs/1907.08982
Code: https://github.com/freelunchtheorem/Conditional_Density_Estimation

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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.