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Top Papers in Variational autoencoders

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An Introduction to Variational Autoencoders

Variational autoencoders provide a principled framework for learning deep
latent-variable models and corresponding inference models. In this work, we
provide an introduction to variational autoencoder

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A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains

Normalizing flows, diffusion normalizing flows and variational autoencoders
are powerful generative models. In this paper, we provide a unified framework
to handle these approaches via Markov chains.

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Diffusion Variational Autoencoders

A standard Variational Autoencoder, with a Euclidean latent space, is
structurally incapable of capturing topological properties of certain datasets.
To remove topological obstructions, we introduce D

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Assessing Differentially Private Variational Autoencoders under Membership Inference

We present an approach to quantify and compare the privacy-accuracy trade-off
for differentially private Variational Autoencoders. Our work complements
previous work in two aspects. First, we evaluate

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Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders

Manifold-valued data naturally arises in medical imaging. In cognitive
neuroscience, for instance, brain connectomes base the analysis of coactivation
patterns between different brain regions on the a

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disentanglement of latent space with variational autoencoders

Disentangling Variational Autoencoders

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Tutorial on Variational Autoencoders

In just three years, Variational Autoencoders (VAEs) have emerged as one of
the most popular approaches to unsupervised learning of complicated
distributions. VAEs are appealing because they are built

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Dimensionality Reduction Techniques for uni and time series data

Dimension Reduction for time series with Variational AutoEncoders

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Learning Probability Distributions in Machine Learning

Latent Variable Modelling Using Variational Autoencoders: A survey

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Robust Variational Autoencoders

Certifiably Robust Variational Autoencoders

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Diffusion Priors In Variational Autoencoders

Among likelihood-based approaches for deep generative modelling, variational
autoencoders (VAEs) offer scalable amortized posterior inference and fast
sampling. However, VAEs are also more and more ou

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Sparse Gaussian Process Variational Autoencoders

Large, multi-dimensional spatio-temporal datasets are omnipresent in modern
science and engineering. An effective framework for handling such data are
Gaussian process deep generative models (GP-DGMs)

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Variational Autoencoders from the perspective of harmonic analysis

Variational Autoencoders: A Harmonic Perspective

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Linear-Time Process Variational Autoencoders

Markovian Gaussian Process Variational Autoencoders

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Gaussian Process Prior Variational Autoencoders

Variational autoencoders (VAE) are a powerful and widely-used class of models
to learn complex data distributions in an unsupervised fashion. One important
limitation of VAEs is the prior assumption t

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Scalable Gaussian Process Variational Autoencoders

Conventional variational autoencoders fail in modeling correlations between
data points due to their use of factorized priors. Amortized Gaussian process
inference through GP-VAEs has led to significa

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DoS and DDoS Mitigation Using Variational Autoencoders

DoS and DDoS attacks have been growing in size and number over the last
decade and existing solutions to mitigate these attacks are in general
inefficient. Compared to other types of malicious cyber a

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InfoVAE: Information Maximizing Variational Autoencoders

A key advance in learning generative models is the use of amortized inference
distributions that are jointly trained with the models. We find that existing
training objectives for variational autoenco

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Multi-Facet Clustering Variational Autoencoders

Work in deep clustering focuses on finding a single partition of data.
However, high-dimensional data, such as images, typically feature multiple
interesting characteristics one could cluster over. Fo

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RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback

Recent research has shown the advantages of using autoencoders based on deep
neural networks for collaborative filtering. In particular, the recently
proposed Mult-VAE model, which used the multinomia

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