Interpretable VAEs for nonlinear group factor analysis

international conference on machine learning, Volume abs/1802.06765, 2018.

Cited by: 12|Views19
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Abstract:

Deep generative models have recently yielded encouraging results in producing subjectively realistic samples of complex data. Far less attention has been paid to making these generative models interpretable. In many scenarios, ranging from scientific applications to finance, the observed variables have a natural grouping. It is often of i...More

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