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)

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet