Hybrid models with deep and invertible features

Hybrid models with deep and invertible features

We propose a neural hybrid model consisting of a linear model defined on a set of features computed by a deep, invertible transformation (i.e. a normalizing flow). An attractive property of our model is that both p(features), the density of the features, and p(targets|features), the predictive distr...

書誌詳細
主要な著者:	Eric, N, Matsukawa, A, Teh, Y, Gorur, D, Lakshminarayanan, B
フォーマット:	Conference item
出版事項:	Proceedings of Machine Learning Research 2019

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