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Learnable Explicit Density for Continuous Latent Space and Variational Inference
In this paper, we study two aspects of the variational autoencoder (VAE)...
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Disentangling Latent Space for VAE by Label Relevant/Irrelevant Dimensions
VAE requires the standard Gaussian distribution as a prior in the latent...
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Enhanced Variational Inference with Dyadic Transformation
Variational autoencoder is a powerful deep generative model with variati...
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Learning to Draw Samples with Amortized Stein Variational Gradient Descent
We propose a simple algorithm to train stochastic neural networks to dra...
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Nonparametric Topic Modeling with Neural Inference
This work focuses on combining nonparametric topic models with Auto-Enco...
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Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
Recently, variational auto-encoder (VAE) based approaches have made impr...
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Variational Autoencoders for Anomalous Jet Tagging
We present a detailed study on Variational Autoencoders (VAEs) for anoma...
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HyperVAE: A Minimum Description Length Variational Hyper-Encoding Network
We propose a framework called HyperVAE for encoding distributions of distributions. When a target distribution is modeled by a VAE, its neural network parameters θ is drawn from a distribution p(θ) which is modeled by a hyper-level VAE. We propose a variational inference using Gaussian mixture models to implicitly encode the parameters θ into a low dimensional Gaussian distribution. Given a target distribution, we predict the posterior distribution of the latent code, then use a matrix-network decoder to generate a posterior distribution q(θ). HyperVAE can encode the parameters θ in full in contrast to common hyper-networks practices, which generate only the scale and bias vectors as target-network parameters. Thus HyperVAE preserves much more information about the model for each task in the latent space. We discuss HyperVAE using the minimum description length (MDL) principle and show that it helps HyperVAE to generalize. We evaluate HyperVAE in density estimation tasks, outlier detection and discovery of novel design classes, demonstrating its efficacy.
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