A new speech enhancement method using variational autoencoders.
problem Improving speech quality in noisy environments.
method Using a variational autoencoder as a speech model, trained with unsupervised noise modeling.
result The method outperforms existing techniques in speech enhancement.
This paper investigates semi-supervised hashing methods using variational autoencoders.
problem Semantic hashing with scarce labels.
method Two semi-supervised approaches: joint modeling and pairwise loss.
result The pairwise approach can improve hash quality with many labeled points but degrades with few labels.
Unified VAE framework improves unsupervised, semi-supervised, and supervised learning.
problem Improving performance in learning tasks with limited labeled data.
method A VAE with a classification layer connected to the encoder and combined with the latent layer for the decoder, supplemented with a supervised loss for labeled data.
result The approach outperforms direct supervised setups and boosts unsupervised tasks with unlabeled data.
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.
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…
This paper presents an infinite variational autoencoder (VAE) whose capacity adapts to suit the input data. This is achieved using a mixture model where the mixing coefficients are modeled by a Dirichlet process, allowing us to integrate over the coefficients when performing inference. Critically, this then allows us t…
A deep learning framework learns meaningful representations for weakly supervised multiple instance learning.
problem Weakly supervised multiple instance learning with uncertainty in positive instance labels.
method Discriminative model regularized by variational autoencoders to learn latent representations.
result Improved performance on standard benchmark datasets compared to state-of-the-art approaches.
Enhances speech in noisy environments using neural networks and NMF.
problem Speaker-independent multichannel speech enhancement in unknown noisy conditions.
method Uses variational autoencoders for supervised speech modeling and NMF for unsupervised noise modeling.
result The proposed approach outperforms NMF-based methods in noisy environments.
Variational autoencoders model water Cherenkov detector data.
problem Modeling generative distribution of water Cherenkov detector data.
method Variational autoencoders and normalizing flows.
result Demonstrated capability of variational autoencoders for generative modelling.
Method separates data into class and style factors using semi-supervised learning.
problem Separating generative factors of data into class and style vectors.
method Independent Vector Variational Autoencoders with semi-supervised learning and independence term.
result Improves classification performance and generation controllability.
Method generates natural ECGs with 25 interpretable features.
problem Lack of labeled ECGs for supervised learning and automatic diagnostics.
method Variational autoencoder for ECG generation and feature extraction.
result Low Maximum Mean Discrepancy (0.00383) indicates good ECG generation quality.
This paper tackles unsupervised speech enhancement using RVAE and proposes efficient sampling methods.
problem Unsupervised speech enhancement with high computational complexity.
method Recurrent variational autoencoder (RVAE) combined with Langevin dynamics and Metropolis-Hasting sampling.
result Sampling-based algorithms outperform VEM and achieve robust generalization.
A method for disentangling latent variables using weak supervision based on pairwise similarities.
problem Disentangling latent variables without strong supervision.
method Weak supervision through binary or real-valued similarities, applied within a Variational Autoencoder framework.
result Utilizing weak supervision improves disentanglement performance substantially.
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.
Improves supervised learning with target-embedding autoencoders.
problem Improving generalization in purely supervised settings with high-dimensional target spaces.
method Target-Embedding Autoencoders (TEA) for jointly optimizing latent representations for prediction and feature predictability.
result Guaranteed generalization for linear TEAs through uniform stability, and empirical validation across multivariate sequence forecasting.
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…
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.
Weak supervision enables learning causal representations from unstructured data.
problem Learning high-level causal representations from unstructured data like images.
method Weakly supervised setting with paired samples before and after interventions. Implicit latent causal models using variational autoencoders.
result Models can reliably identify causal structure and disentangle causal variables.
Proposes a VAE with Student-t mixture model for authorship attribution.
problem Traditional authorship attribution in closed-set scenarios.
method Extends variational autoencoder with embedded Student-t mixture model. result Superior performance over existing methods on Amazon review dataset.
We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference. This allows us to both perform reasoning (e.g. classification) under the structural constraints…
Graph semi-supervised learning classifies points on manifold using variational autoencoders and GNN.
problem Classifying points on low-dimensional manifolds using limited labeled data.
method Model data as points on a manifold, approximate manifold with VAE, construct geometric graph, solve semi-supervised node classification with GNN.
result Generalization gap diminishes with graph size and training procedure, vanishing asymptotically.
Proposes KIL-AdaVAE for fault detection and segmentation of unknown fault types.
problem Lack of labeled data for fault types in safety-critical systems.
method Implicit supervision with Deep Variational Autoencoders (VAE).
result Significant performance improvements in fault detection and segmentation.
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.
Speech enhancement improved using variational autoencoders and alpha-stable noise models.
problem Improving speech quality in noisy environments.
method Deep generative model using variational autoencoders with alpha-stable noise model.
result The proposed method outperforms conventional approaches in speech intelligibility and quality.
Improved SVAE models enhance sequential data prediction.
problem Challenges in implementing and using structured variational autoencoders.
method Modern machine learning tools, hardware acceleration, parallelization, automatic differentiation, exploiting structure in the prior.
result SVAEs outperform general alternatives in accuracy and efficiency.
Proposes Dirichlet Variational Autoencoder (DirVAE) for better latent representation.
problem Improving latent representation in autoencoders.
method Uses Dirichlet prior and stochastic gradient method with inverse Gamma approximation to address collapsing issues.
result DirVAE outperforms baselines in log-likelihood and classification accuracy.
Unified VAE for brain aging analysis improves regression accuracy.
problem Applying VAE to supervised learning for brain imaging data.
method Unified probabilistic model for latent space learning with conditional distribution modeling.
result Model predicts age from MR images more accurately than state-of-the-art methods.
New technique learns causally disentangled representations for better generation.
problem Learning disentangled representations for accurate generation.
method Causally Disentangled Generation (CDG) approach with supervised regularization.
result CDG is necessary and sufficient for accurate disentangled generation.
This paper proposes a new method to select labeled data points using VAEs for active learning.
problem High cost of acquiring labels in supervised machine learning.
method Data-driven approach using Variational Autoencoders (VAEs) to select a diverse core-set in a low-dimensional latent space.
result Improvement in accuracy over related techniques, highlighting the representation power of generative modeling.
Paper proposes a new autoencoder metric for balanced learning in imbalanced tabular datasets.
problem Challenges of imbalanced self-supervised learning in tabular data.
method Developed a Multi-Supervised Balanced MSE metric to balance learning.
result The new metric outperforms standard MSE in imbalanced datasets.
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…
A framework for disentangling class-related and class-independent factors in data.
problem Learning disentangled representations in variational autoencoders.
method Attention mechanism in latent space, mixture models, Bhattacharyya coefficient, semi-supervised training.
result Disentangles class-related and class-independent factors of variation.
Two VAE-based methods improve anomaly detection in semi-supervised settings.
problem Anomaly detection with limited labeled data.
method Intuitive idea of separating latent vectors for normal and outlier data, derived from probabilistic formulations.
result Marked improvement in outlier detection compared to state-of-the-art methods.
New framework improves generative models with prediction and consistency constraints.
problem Improving generative models with sparse labeled data.
method Optimizes variational autoencoders with prediction and consistency constraints.
result Promising image classification performance, especially in semi-supervised scenarios.
Paper proposes semi-supervised learning for bearing anomaly detection.
problem Challenges in obtaining accurate labels for bearing fault diagnosis.
method Uses deep variational autoencoders for semi-supervised learning.
result Improves anomaly detection accuracy by 3% to 30% using semi-supervised learning.
EXoN creates an explainable latent space for semi-supervised learning.
problem Creating an explainable latent space for semi-supervised learning.
method EXoN combines VAE with SCI (Soft-label Consistency Interpolation) to create an explainable latent space.
result EXoN reduces the cost of investigating representation patterns on the latent space.
Gaussian process variational autoencoders improve disentanglement in time series data.
problem Learning disentangled representations from multivariate time series data.
method Model each latent channel with a Gaussian process prior and a structured variational distribution to capture temporal dependencies.
result Competitive performance on benchmark and real-world medical time series data.
DVSDR reduces data dimensions while preserving label information.
problem Sufficient dimensionality reduction of high-dimensional observations.
method Deep variational approach using variational autoencoders.
result DVSDR performs competitively on classification tasks and generates novel data.
PepCVAE designs novel antimicrobial peptides using a semi-supervised VAE.
problem Designing novel antimicrobial peptides for next-generation antimicrobial resistance solutions.
method Semi-supervised variational autoencoder (VAE) model that learns latent and antimicrobial attribute spaces from unlabeled and labeled data.
result PepCVAE generates novel AMPs with higher long-range diversity and closer to biological peptide distribution.
MAVENs combine GAN and VAE for better image generation and classification.
problem Improve image generation and classification using unsupervised learning.
method Introduce MAVENs, an ensemble of discriminators in a VAE-GAN network.
result Demonstrated competitive performance in image generation and classification tasks.
SOS-VAE improves generative models for scientific applications by correcting decoder bias.
problem Bias in generative parameters due to supervised learning in VAEs.
method Develops SOS-VAE framework to influence decoder for predictive latent representation.
result Ensures reliable generative parameters for scientific applications.
Paper proposes CCVAE for generalized zero-shot domain adaptation.
problem Adapting to unseen classes in target domain with limited labeled data.
method Coupled Conditional Variational Autoencoder (CCVAE).
result CCVAE generates synthetic target domain features for unseen classes.
S-VQ-VAE learns interpretable class-specific representations.
problem Learning interpretable representations of data.
method Supervised Vector Quantized Variational AutoEncoder (S-VQ-VAE).
result S-VQ-VAE learns interpretable class-specific representations.
This paper introduces a quantization-based regularizer for autoencoders to improve latent representations.
problem Autoencoders can overfit and collapse, leading to poor latent representations.
method The authors combine VQ-VAE and denoising methods to introduce a bottleneck Bayesian estimator that soft quantizes latent codes.
result The method results in better latent representations for supervised and clustering tasks.
Generative concept representations improve deep learning by handling uncertainty and integrating learning and reasoning.
problem Discriminative deep learning struggles with uncertainty and lacks integration of learning and reasoning.
method Probabilistic and generative deep learning, variational autoencoders, and generative adversarial networks.
result Generative concept representations enhance deep learning by addressing these limitations.
Develops a flexible deep autoencoding topic model with scalable hybrid Bayesian inference.
problem Flexible and interpretable document analysis models.
method DATM with hybrid Bayesian inference, including topic-layer-adaptive stochastic gradient Riemannian MCMC and Weibull variational encoder.
result Demonstrates scalability and efficacy on big corpora in unsupervised and supervised learning tasks.
VDA improves disentanglement of latent representations in complex signals.
problem Learning disentangled and interpretable representations in nonstationary, high-dimensional time-evolving signals.
method Variational decomposition autoencoding (VDA) framework, incorporating signal decomposition, contrastive self-supervised task, and variational prior approximation.
result DecVAEs surpass state-of-the-art VAE-based methods in disentanglement quality and generalization.
Automates galaxy morphology classification with less human labelling.
problem Insufficient human-labeled galaxy images for accurate classification.
method Developed a VAE with equivariant transformer layers and a classifier network.
result Improves accuracy with fewer labels and unlabelled data.