The paper interprets VQ-VAE loss as a form of information bottleneck.
problem Understanding the VQ-VAE loss function.
method Interpreted VQ-VAE loss as variational deterministic information bottleneck (VDIB) and variational information bottleneck (VIB).
result VQ-VAE loss can be derived from VDIB and approximated by VIB.
PRI-VAE learns disentangled representations by optimizing principle-of-relevant-information.
problem Learning disentangled representations under VAE framework remains unknown.
method Proposes PRI-VAE, a novel learning objective to optimize disentanglement.
result Demonstrates effectiveness of PRI-VAE on four benchmark datasets.
We extend Stochastic Gradient Variational Bayes to perform posterior inference for the weights of Stick-Breaking processes. This development allows us to define a Stick-Breaking Variational Autoencoder (SB-VAE), a Bayesian nonparametric version of the variational autoencoder that has a latent representation with stocha…
Study reveals the regularization effect of variational distributions in VAEs.
problem Understanding the regularization role of variational distributions in VAEs.
method Analyzed the role of variational family in VAEs and studied the regularization effect on local geometry.
result Uncovered the implicit regularizer in the β-VAE objective and proposed a deterministic autoencoding objective. Derives M2VAE objective from marginal joint log-likelihood.
problem Training Multi-Modal Variational Autoencoders (M2VAEs). method Derives trainable evidence lower bound from marginal joint log-likelihood.
result Derives M2VAE objective from marginal joint log-likelihood. ED-VAE improves VAEs by explicitly including entropy components in ELBO.
problem Limitations of traditional VAEs with ELBO in generating high-quality samples and interpreting latent spaces.
method Introduces ED-VAE, a re-formulation of ELBO that includes entropy and cross-entropy components.
result Significantly enhances model flexibility and improves interpretability and generative performance.
This work tackles posterior collapse in conditional and hierarchical VAEs.
problem Posterior collapse in VAEs leads to poor latent variable representations.
method Theoretical analysis of linear conditional and hierarchical VAEs, empirical validation.
result Theoretical and empirical evidence of posterior collapse causes in conditional and hierarchical VAEs.
DAEs can generate images without additional loss terms, inheriting VAE properties.
problem Difficulty in using VAEs for practical generative modelling.
method Empirical exploration of DAEs for image generation without novel methods.
result DAEs can generate images successfully without additional loss terms.
The paper provides convergence guarantees for VAEs using SGD and Adam.
problem Understanding theoretical convergence guarantees for VAEs.
method Derives non-asymptotic convergence rates for VAEs trained with SGD and Adam.
result Convergence rate of \(\mathcal{O}(\log n / \sqrt{n})\) with explicit hyperparameter dependencies.
tvGP-VAE models tensor-valued latent variables with Gaussian processes for better data structure representation.
problem Agnostic latent variables in VAEs ignore data structure correlations.
method Proposes tensor-variate Gaussian process prior for variational autoencoder.
result Explicitly modeling correlation structures improves model performance in reconstruction.
NE-VAE prevents posterior collapse in VAEs by embedding neighbors in latent space.
problem Posterior collapse in VAEs when strong decoders are used.
method Neighbor embedding in latent space to prevent collapse.
result NE-VAE produces qualitatively different latent representations with active latent dimensions.
This tutorial derives the VAE loss function under Gaussian assumptions.
problem Computational intractability of posterior distributions in Bayesian machine learning.
method Derives the variational lower bound loss function of a standard VAE.
result The Kullback-Leibler divergence has a closed form solution under Gaussian assumptions.
Develops a new VAE for mixed-curvature latent spaces.
problem Lack of a generic approach for VAEs in curved latent spaces.
method Mixed-curvature Variational Autoencoder (MVAE) that trains on a product of constant curvature Riemannian manifolds.
result Generalizes Euclidean VAE to curved latent spaces and recovers Euclidean VAE when curvatures are zero.
Lower bound for VAE training objective for binary data.
problem Finding a lower bound for the ELBO of Bernoulli VAE.
method Interpretable lower bound, modified initialization, faster training architecture, PCA for latent space dimension.
result Theoretical result and improved performance of new architecture.
Study shows how to manipulate VAEs for attacks and assess their robustness.
problem Adversarial attacks on Variational Autoencoders (VAE).
method Examine modifications to VAEs and propose metrics for robustness.
result Metrics to quantify the robustness of VAEs to adversarial attacks.
We present two deep generative models based on Variational Autoencoders to improve the accuracy of drug response prediction. Our models, Perturbation Variational Autoencoder and its semi-supervised extension, Drug Response Variational Autoencoder (Dr.VAE), learn latent representation of the underlying gene states befor…
Proposes NC-VAE to prevent posterior collapse in VAEs.
problem Posterior collapse in variational autoencoders (VAEs).
method Noise contrastive estimation applied to VAEs.
result Proves NC-VAE cannot reach posterior collapse and provides lower bounds.
CoVAE improves VAEs by reducing training steps and improving quality.
problem Two-stage training overhead and increased sampling times.
method Consistency training of VAEs with progressive latent representations and time-dependent β parameter.
result CoVAE generates high-quality samples in one or few steps.
The variational autoencoder (VAE) is a generative model with continuous latent variables where a pair of probabilistic encoder (bottom-up) and decoder (top-down) is jointly learned by stochastic gradient variational Bayes. We first elaborate Gaussian VAE, approximating the local covariance matrix of the decoder as an o…
Proposes a method to learn conditional VAEs from datasets with missing covariates.
problem Learning conditional VAEs from datasets with missing covariates.
method Augments conditional VAEs with a prior distribution for missing covariates and estimates their posterior using amortised variational inference.
result The proposed method outperforms previous methods in learning conditional VAEs from non-temporal, temporal, and longitudinal datasets.
New methods improve genetic studies of complex diseases.
problem Improving genetic studies of complex diseases using high-dimensional clinical data.
method Evaluation of unsupervised disentangled representation learning methods (autoencoders, VAE, beta-VAE, FactorVAE) for genetic association studies.
result FactorVAEs and beta-VAEs outperform standard VAEs and non-variational autoencoders in genetic studies of asthma and COPD.
Paper uses VAEs to detect radar targets in complex noise.
problem Detecting radar targets in compound clutter and thermal noise.
method Proposes a VAE architecture to distinguish radar targets from various noise types.
result The VAE outperforms classical detectors in challenging noise conditions.
IDVAE combines VAE and GAN without explicit discriminator.
problem Combining VAE and GAN for reconstruction and generation.
method Shared encoder and discriminator with combined decoder and generator.
result IDVAE outperforms state-of-the-art hybrid approaches.
This work provides statistical guarantees for VAEs using PAC-Bayesian theory.
problem Theoretical properties of VAEs remain open questions.
method PAC-Bayesian theory to derive statistical guarantees.
result Upper bounds on Wasserstein distance between input and generative model.
GD-VAEs learn dynamics from observations using geometric and topological information.
problem Learning parsimonious representations of nonlinear dynamics from observations.
method Develops data-driven methods incorporating geometric and topological information using Variational Autoencoders (VAEs).
result GD-VAEs provide methods for learning reduced dimensional representations of nonlinear dynamics.
Certifiably robust VAEs are trained with bounds on input perturbations.
problem Ensuring VAEs are robust to adversarial attacks.
method Derive bounds on minimal perturbation size, control parameters, and train VAEs to meet criteria.
result Certifiably robust VAEs are more robust to attacks than standard VAEs.
Improves VAE training by refining variational parameters with BSVI.
problem Amortized inference in VAEs leads to suboptimal variational parameters and the amortization gap.
method Proposes BSVI, a refinement procedure using SVI's importance weights.
result Training VAEs with BSVI yields improved performance compared to SVI.
Robust VAE improves model performance on corrupted data.
problem Outliers in training data degrade model performance.
method Applying robust statistics to VAEs using beta-divergence.
result Improved robustness to outliers in generated representations.
Improved VAE estimation from incomplete data using variational mixtures.
problem Estimating VAEs from incomplete data increases posterior complexity.
method Introducing variational mixtures based on finite and imputation distributions.
result Variational mixtures improve VAE estimation accuracy from incomplete data.
GPPVAE combines VAEs with GP priors for better time-series data modeling.
problem Strong independence assumption in VAEs limits their performance on time-series data.
method Introduces GPPVAE model with GP priors and new stochastic backpropagation.
result GPPVAE outperforms CVAEs and standard VAEs in image data applications.
This work improves VAEs using MCMC methods for better variational bounds.
problem Improving the expressiveness of variational distributions in VAEs.
method Entropy-based adaptation for MALA/HMC chains to optimize tighter variational bounds.
result Higher held-out log-likelihoods and improved generative metrics.
VAEs (Variational AutoEncoders) have proved to be powerful in the context of density modeling and have been used in a variety of contexts for creative purposes. In many settings, the data we model possesses continuous attributes that we would like to take into account at generation time. We propose in this paper GLSR-V…
Hybrid VAE improves movie recommendation accuracy.
problem Improving personalized recommendations in online marketplaces.
method Combining movie embeddings from a sibling VAE network with user ratings for movie recommendation.
result Empirical evidence shows VAE network benefits from incorporating movie embeddings.
New VAE limits latent layer information rate for better performance.
problem Improving latent layer information in VAEs.
method Imposes a signal-to-noise ratio on latent layer information.
result BIR-VAE provides meaningful latent representation with specified information rate.
InVA models image outcomes from multiple modalities, outperforming standard VAEs.
problem Understanding relationships across multiple imaging modalities in neuroimaging.
method Integrative Variational Autoencoder (InVA) framework for image-on-image regression.
result InVA accurately predicts PET scans from structural MRI, outperforming conventional models.
Proposes using Monte Carlo Dropout in Autoencoder and VAE for synthetic data generation.
problem Handling large amounts of data in costly or difficult-to-collect scenarios.
method Incorporates Monte Carlo Dropout within Autoencoder and Variational Autoencoder.
result Generated data sets are statistically and predictively similar to actual data.
New GP-VAE model improves scalability and performance.
problem Inability of conventional VAEs to model correlations between data points.
method Principled sparse inference approaches to improve scalability of GP-VAEs.
result New model outperforms existing approaches in runtime and memory usage.
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 on top of standard function approximators (neural networks), and can be trained with stochastic gradient descent. VAEs …
C-VAE improves VAE by resolving prior issues and generating better samples.
problem Low-quality samples from VAE due to prior issues.
method Formulates VAE as OT, allows flexible priors, and uses OT formulations.
result C-VAE generates higher quality samples and latent representations.
The paper presents the application of Variational Autoencoders (VAE) for data dimensionality reduction and explorative analysis of mass spectrometry imaging data (MSI). The results confirm that VAEs are capable of detecting the patterns associated with the different tissue sub-types with performance than standard appro…
This paper bridges statistical and machine learning approaches to variational inference.
problem Statisticians struggle to understand variational inference from a Frequentist perspective.
method Explains VI, VAEs, and DDMs from a Frequentist viewpoint, starting with EM.
result VI emerges as a scalable solution for intractable E-steps in VAEs and DDMs.
VAE models simulate chaotic particle trajectories in flames.
problem Simulating chaotic particle trajectories in combustion systems.
method Variational autoencoder trained on 3D reconstructed particle trajectories.
result Generated trajectories match experimental data accurately.
VAEs analyzed using harmonic analysis, showing how variance controls frequency content and robustness.
problem Understanding and optimizing VAEs for robustness and frequency control.
method Viewing VAE latent space as Gaussian space, deriving results on variance and frequency content, and demonstrating soft Lipschitz constraints.
result Increasing encoder variance reduces high frequency content and improves adversarial robustness.
Score matching fails to train VAEs robustly, revealing autoencoding loss insights.
problem Catastrophic failure of variational score matching on VAE models.
method Analysis of existing variational score matching objectives and their equivalence to autoencoding losses.
result Score matching methods fail to produce robust VAE models, predicting poor performance.
Proposes a multi-view VAE for imputing missing data from correlated sources.
problem Imputing missing data from multi-view sources with latent space correlation.
method Enforces a joint prior with latent space correlation between VAEs trained on each view.
result More strongly correlated latent spaces are uncovered, enabling effective imputation.
Proposes a new VAE framework for anomaly detection in time series data.
problem Data scarcity leads to latent holes and discontinuous regions in latent space, causing non-robust reconstructions.
method Combines VAEs with self-supervised learning to address data scarcity and improve anomaly detection.
result Improves robustness of anomaly detection in time series data by addressing latent holes and discontinuities.
New framework scales graph AE and VAE by training on a subset of nodes.
problem Training scalability and speed issues in graph AE and VAE models.
method Utilizes graph degeneracy to train on a dense subset of nodes, with a propagation mechanism.
result Empirically competitive results on large graphs (millions of nodes and edges).
New graph AE and VAE model predicts directed links better than existing methods.
problem Link prediction in directed graphs, especially for unobserved edges.
method Gravity-inspired decoder scheme for directed graphs.
result Outperforms standard graph AE and VAE on three real-world directed link prediction tasks.