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Found 6 papers, 4 papers with code
Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling
We also introduce gated structured layers, which allow bypassing the parts of the models that fail to capture the statistics of the data.
Automatic structured variational inference
However, the performance of the variational approach depends on the choice of an appropriate variational family.
Likelihood Ratios for Out-of-Distribution Detection
We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Modern machine learning methods including deep learning have achieved great success in predictive accuracy for supervised learning tasks, but may still fall short in giving useful estimates of their predictive {\em uncertainty}.
Dueling Decoders: Regularizing Variational Autoencoder Latent Spaces
Variational autoencoders learn unsupervised data representations, but these models frequently converge to minima that fail to preserve meaningful semantic information.
$β$-VAEs can retain label information even at high compression
In this paper, we investigate the degree to which the encoding of a $\beta$-VAE captures label information across multiple architectures on Binary Static MNIST and Omniglot.
