Theoretical advantages are reflected in experimental results. Diederik P Kingma, et al., "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114 通常の自己符号化器(Autoencoder)とは異なり、観測されたデータがある確率分布に基づいて…

Second, we show that for i.i.d. datasets with continuous latent variables per datapoint, posterior inference can be made especially efficient by fitting an approximate inference model (also called a recognition model) to the intractable po…

First, we show that a reparameterization of the variational lower bound yields a lower bound estimator that can be straightforwardly optimized using standard stochastic gradient methods. Diederik P Kingma, et al., "Auto-Encoding Variationa…

Our contributions is two-fold. Diederik P Kingma, et al., "Auto-Encoding Variational Bayes" https://arxiv.org/abs/1312.6114 通常の自己符号化器(Autoencoder)とは異なり、観測されたデータがある確率分布に基づいて生成されたと仮定する変分自己符号…

We introduce a stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case. Diederik P Kingma, et al., "Auto-Encoding Variation…

How can we perform efficient inference and learning in directed probabilistic models, in the presence of continuous latent variables with intractable posterior distributions, and large datasets? Diederik P Kingma, et al., "Auto-Encoding Va…