A new autoencoder architecture captures multiscale data.
problem Multiscale spatio-temporal data representation.
method Integrates multigrid methods, convolutional autoencoders, and transfer learning.
result Adaptive, hierarchical architecture captures different scaled features dynamically.
We provide a series of results for unsupervised learning with autoencoders. Specifically, we study shallow two-layer autoencoder architectures with shared weights. We focus on three generative models for data that are common in statistical machine learning: (i) the mixture-of-gaussians model, (ii) the sparse coding mod…
Deep autoencoder network solves spectrum sharing problems efficiently.
problem Resource allocation in wireless communications and D2D networks.
method Generative neural network (autoencoder) for solving linear sum assignment problems.
result Hybrid autoencoder architecture outperforms other methods in accuracy and speed.
A new autoencoder speeds up audio style transfer.
problem Efficiently generating stylized audio from content audio.
method Autoencoder architecture for real-time audio style transfer.
result Single forward pass for stylized audio generation.
Paper proposes a faster RAE with sequence-aware encoding.
problem Training recurrent autoencoders is challenging and time-consuming.
method Introduces a recurrent autoencoder with sequence-aware encoding using 1D convolutional layers.
result The proposed autoencoder trains faster than standard RAE.
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.
Deterministic training improves generative autoencoder performance.
problem Stochastic training limits generative autoencoder performance.
method Invertible layers for deterministic training.
result AEFs outperform VAEs in log-likelihood and sample quality.
FisherNet extends Autoencoder using Fisher information for better data reconstruction.
problem Data reconstruction accuracy and model scalability in high-dimensional latent spaces.
method Introduces FisherNet architecture that uses Fisher information to quantify and account for latent space uncertainty.
result FisherNet produces more accurate reconstructions and scales better with latent space dimensions compared to VAE.
We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to ad…
Smooth embedding space improves NAS performance.
problem Efficiently predicting good neural architectures.
method Two-sided variational graph autoencoder.
result Smooth embedding space facilitates extrapolation to unseen architectures.
Paper proposes a new Autoencoder for robustly encoding white matter streamlines.
problem Limited Autoencoder architectures ignore global streamline geometry and lack interpretability.
method Introduces Differentiable Vector Quantized Variational Autoencoder (D-VQ-VAE) for entire streamline bundles.
result Demonstrates superior performance in encoding and synthesis compared to state-of-the-art Autoencoders.
DECAF-GAD improves fairness in autoencoder-based GAD models without sacrificing performance.
problem Fairness in autoencoder-based GAD models for node-level anomaly detection.
method DECAF-GAD uses a structural causal model to disentangle sensitive attributes from learned representations, along with a fairness-guided loss function.
result DECAF-GAD significantly enhances fairness metrics while maintaining anomaly detection performance.
Improved SINDy autoencoder for identifying noisy dynamical systems.
problem Robust identification of noisy dynamical systems from data.
method Incorporates noise-separating neural network structures into SINDy autoencoder architecture.
result Accurately recovers latent dynamics and estimates measurement noise from noisy observations.
daep learns from irregular, multimodal astronomical data.
problem Learning from irregular, multimodal astronomical sequences.
method Diffusion Autoencoder with Perceivers (daep) tokenizes, compresses, and reconstructs data.
result daep outperforms VAE and maep baselines in reconstruction and fine-scale structure preservation.
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.
CGNP embeds functional processes into latent vectors using graph neural networks.
problem Embedding and sampling functional processes over arbitrary domains.
method CGNP employs graph neural networks to embed and decode functional processes.
result CGNP effectively samples encoded functions over any domain.
New autoencoder learns structured representations without regularization.
problem Learning structured representations without relying on regularization.
method Proposes a novel autoencoder architecture that learns a hierarchy of latent variables.
result Improves results in generation, disentanglement, and extrapolation tasks.
Unified model trained on images and videos using masked autoencoding.
problem Training a single model for multiple visual modalities.
method Masked autoencoding on a Vision Transformer.
result Unified model achieves comparable or better performance than single-modality models.
New autoencoder framework learns structured latent priors.
problem Learning autoencoders with flexible priors.
method Relational regularization on latent prior, scalable algorithms.
result RAE outperforms existing autoencoders in image generation.
We examine two fundamental tasks associated with graph representation learning: link prediction and semi-supervised node classification. We present a novel autoencoder architecture capable of learning a joint representation of both local graph structure and available node features for the multi-task learning of link pr…
A graph VAE framework optimizes neural architectures in a continuous space.
problem Discovering efficient neural architectures in a discrete space.
method Graph VAE framework with VAE and GNN components, joint learning of predictors and decoders.
result The framework discovers powerful neural architectures with both excellent performance and high computational efficiency.
This study analyzes VAEs using ID and II, revealing a transition in behaviour and distinct training phases.
problem Understanding the hidden representations and training phases of VAEs.
method Analysis using Intrinsic Dimension (ID) and Information Imbalance (II).
result VAEs exhibit a transition in behaviour and distinct training phases when the bottleneck size exceeds the Intrinsic Dimension of the data.
This paper compares different deep learning techniques for feature learning from EHRs.
problem Extracting meaningful insights from high-dimensional, sparse clinical data.
method Uses stacked sparse autoencoders, deep belief networks, adversarial autoencoders, and variational autoencoders for feature representation.
result Variational autoencoders outperform other methods for large data sets, while stacked sparse autoencoders are superior for small data sets.
D-VAE generates valid DAGs for neural architecture search and Bayesian network learning.
problem Generating valid DAGs for machine learning models.
method Proposes a novel DAG variational autoencoder (D-VAE) using graph neural networks and asynchronous message passing.
result Demonstrates the effectiveness of D-VAE through neural architecture search and Bayesian network structure learning.
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.
A new model for multiview data analysis using graph autoencoders.
problem Nonlinear multiview canonical correlation analysis for large datasets.
method Variational approach with graph convolutional neural networks.
result Competitive performance on classification, clustering, and recommendation tasks.
Combines variational autoencoders with normalizing flows for faster training.
problem Training normalizing flow models like Glow is slow and requires deep architectures.
method Integrates Glow with a variational autoencoder to speed up training.
result The combined model achieves similar image quality and likelihood to Glow but trains faster.
MoCA uses a novel autoencoder to analyze multi-modal health data.
problem Challenges in analyzing continuous multi-modal health data from wearable devices.
method Proposes MoCA, a self-supervised learning framework combining transformer and masked autoencoder methods.
result Demonstrates strong performance boosts across reconstruction and classification tasks.
VANO uses neural operators for unsupervised learning of functional data.
problem Learning operators between infinite dimensional spaces for functional data.
method Variational Autoencoding Neural Operators (VANO) approach.
result VANO can learn and reconstruct functional data without supervision.
Study on autoencoder denoising in high dimensions.
problem Denoising data from Gaussian mixtures.
method Two-layer non-linear autoencoder with skip connection in high-dimensional limit.
result Closed-form expressions for denoising mean-squared test error.
Study shows double and triple descent in unsupervised autoencoders, improving performance in various tasks.
problem Exploring the phenomenon of double descent in unsupervised learning.
method Analytical demonstration and extensive experiments on synthetic and real datasets.
result Over-parameterized unsupervised autoencoders exhibit double and triple descent, enhancing performance in downstream tasks.
GAN-AE generates synthetic data for imbalanced sequence classification.
problem Imbalanced sequence classification in medical devices.
method Autoencoder and GAN architecture for generating synthetic data.
result GAN-AE outperforms other synthetic data generation methods.
End-to-end policy learning improves statistical arbitrage trading.
problem Traditional mean reversion trading strategies in statistical arbitrage are limited.
method We use Autoencoder architectures and policy learning to develop trading strategies.
result End-to-end training yields superior gross returns.
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR clinical data. A key requirement however is obtaining meaningful insights from high dimensional, sp…
Wasserstein Autoencoders improve model efficiency and interpretability for low-dimensional data.
problem Limited statistical guarantees for WAEs in low-dimensional data.
method Proper network architecture selection and analysis of expected excess risk convergence rates.
result WAEs can learn data distributions efficiently when intrinsic dimension is considered.
Novel variational autoencoder for generative and classification tasks.
problem Developing a robust generative model for various tasks.
method A novel variational autoencoder with specific latent variables and ordinality enforcement.
result Comparable performance in generative and classification tasks compared to baselines.
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…
Proposes a symmetric graph autoencoder for unsupervised learning.
problem Graph representation learning without labeled data.
method Symmetric graph convolutional autoencoder with Laplacian sharpening and signed graphs.
result Outperforms state-of-the-art algorithms in clustering, link prediction, and visualization tasks.
FRAE compresses speech data efficiently with low bitrate.
problem Online compression of sequential data with temporal dependency.
method Feedback Recurrent AutoEncoder architecture with learned prior and entropy coder.
result Achieves high-quality speech waveforms at low fixed bitrate and variable bitrate.
Chart autoencoders learn latent features preserving manifold topology and geometry, with robust denoising capabilities.
problem Learning low-dimensional latent features of high-dimensional data sampled near a manifold.
method Chart autoencoders encode data into latent features on charts, preserving manifold topology and geometry.
result Chart autoencoders achieve a squared generalization error of n−d+22log4n under proper network architectures. A new autoencoder uses stochastic functions to encourage diversity in generated samples.
problem Generating diverse samples from autoencoders.
method Replacing the adversary in AAE with a space of stochastic functions.
result More diverse set of generated samples.
A new architecture improves VAE disentanglement without explicit supervision.
problem Learning disentangled representations in VAEs.
method Spatial Broadcast Decoder: tiling latent vector, concatenating coordinates, fully convolutional network.
result Improves disentangling, reconstruction accuracy, and generalization.
We investigate the problem of learning representations that are invariant to certain nuisance or sensitive factors of variation in the data while retaining as much of the remaining information as possible. Our model is based on a variational autoencoding architecture with priors that encourage independence between sens…
Adversarial autoencoders improve anomaly detection in images.
problem Anomaly detection in images is challenging when training data contains outliers.
method Adversarial autoencoders enforce a prior distribution on latent representations to identify and reject potential anomalies during training.
result Adversarial autoencoders significantly improve robustness to outliers during training.
A new generator uses kernel distance to avoid GAN weaknesses.
problem Stability and mode collapse in GANs and autoencoders.
method LCW generator (Latent Cramer-Wold generator) using kernel distance.
result Very competitive FID values.
Improves stability in hyperbolic neural networks for complex data generation.
problem Numerical instability in hyperbolic neural networks hinders complex architecture development.
method Proposes a novel hyperbolic AE-GAN architecture with stable layers.
result Demonstrates state-of-the-art performance in generating complex data.
Deep autoencoder detects anomalies in wastewater sensor data.
problem Anomaly detection in in-situ wastewater sensor data.
method 1D Convolutional Neural Network (CNN) autoencoder for anomaly detection.
result Validation on in-sewer process monitoring data shows effective anomaly detection.
AEGCN uses autoencoder constraints to improve graph node classification.
problem Node classification on graph domains with reduced information loss.
method Autoencoder-constrained graph convolutional network (AEGCN).
result Adding autoencoder constraints significantly improves graph convolutional network performance.