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Self Normalizing Flows
Efficient gradient computation of the Jacobian determinant term is a core
problem in many machine learning settings, and especially so in the normalizing
flow framework. Most proposed flow models therefore either restrict to a
function class with easy evaluation of the Jacobian determinant, or an
efficient estimator thereof. However, these restrictions limit the performance
of such density models, frequently requiring significant depth to reach desired
performance levels. In this work, we propose Self Normalizing Flows, a flexible
framework for training normalizing flows by replacing expensive terms in the
gradient by learned approximate inverses at each layer. This reduces the
computational complexity of each layer's exact update from
to , allowing for the training of flow architectures which
were otherwise computationally infeasible, while also providing efficient
sampling. We show experimentally that such models are remarkably stable and
optimize to similar data likelihood values as their exact gradient
counterparts, while training more quickly and surpassing the performance of
functionally constrained counterparts
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