Forward Amortized Inference for Likelihood-Free Variational Marginalization

29 May 2018Luca AmbrogioniUmut GüçlüJulia BerezutskayaEva W. P. van den BorneYağmur GüçlütürkMax HinneEric MarisMarcel A. J. van Gerven

In this paper, we introduce a new form of amortized variational inference by using the forward KL divergence in a joint-contrastive variational loss. The resulting forward amortized variational inference is a likelihood-free method as its gradient can be sampled without bias and without requiring any evaluation of either the model joint distribution or its derivatives... (read more)

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