# Predicting time-varying distributions with limited training data

@article{Kou2018PredictingTD, title={Predicting time-varying distributions with limited training data}, author={C. Kou and H. Lee and Teck Khim Ng and Jorge Sanz}, journal={arXiv: Learning}, year={2018} }

In the task of distribution-to-distribution regression, a recently proposed model, distribution regression network (DRN) (Kou et al., 2018) has shown superior performance compared to conventional neural networks while using much fewer parameters. The key novelty of DRN is that it encodes an entire distribution in each network node, and this compact representation allows DRN to achieve better accuracies than conventional neural networks. However, the experiments in Kou et al. (2018) focused… Expand

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