Learning to Draw Samples with Amortized Stein Variational Gradient Descent

20 Jul 2017Yihao FengDilin WangQiang Liu

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient direction (Liu & Wang, 2016) that maximally decreases the KL divergence with the target distribution... (read more)

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