SidAE combines autoencoders and Siamese networks for self-supervised feature extraction.
problem Efficiently extract meaningful features from unlabeled data.
method Proposes SidAE, a combination of autoencoders and Siamese networks for self-supervised image classification.
result SidAE outperforms self-supervised baselines across multiple datasets and scenarios, especially with limited labeled data.
Enhanced financial forecasting using supervised autoencoders with noise augmentation and triple labeling.
problem Improving investment strategy performance on noisy financial data.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced noise augmentation and bottleneck size significantly boost strategy effectiveness.
Autoencoders are widely used for unsupervised learning and as a regularization scheme in semi-supervised learning. However, theoretical understanding of their generalization properties and of the manner in which they can assist supervised learning has been lacking. We utilize recent advances in the theory of deep learn…
Enhances supervised visualization for unseen data using autoencoders and random forest.
problem Lack of generalization to unseen test sets in supervised dimensionality reduction.
method Combines autoencoder and random forest proximities for out-of-sample extension.
result 40% reduction in training time with 10% of training data, achieving consistent quality.
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.
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.
Enhanced financial forecasting with supervised autoencoders for S&P 500 and cryptocurrencies.
problem Improving investment strategy performance in financial markets.
method Supervised autoencoders with noise augmentation and triple barrier labeling.
result Supervised autoencoders with balanced parameters significantly boost strategy effectiveness.
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.
Deep Autoencoder outperforms in anomaly detection for building energy data.
problem Automated detection of faulty data in learning applications.
method Training and comparison of Simple, Deep, and Supervised Deep Autoencoders on ASHRAE building energy dataset.
result Supervised Deep Autoencoder outperforms in total anomalies detected.
New JSA autoencoders tackle discrete latent variable models for semi-supervised learning.
problem Handling discrete observations and latent codes in deep generative models.
method Joint-stochastic-approximation (JSA) autoencoders that directly maximize data log-likelihood and minimize KL divergence.
result JSA autoencoders achieve comparable performance to continuous latent space models in semi-supervised tasks.
We show how a deep denoising autoencoder with lateral connections can be used as an auxiliary unsupervised learning task to support supervised learning. The proposed model is trained to minimize simultaneously the sum of supervised and unsupervised cost functions by back-propagation, avoiding the need for layer-wise pr…
AEALT uses autoencoders to reduce text embedding dimensions for improved efficiency.
problem High dimensionality of text embeddings hinders downstream tasks.
method Factor-augmented supervised learning with autoencoders.
result AEALT outperforms conventional deep-learning approaches.
BSAC improves credit scoring models by leveraging autoencoders and addressing imbalanced datasets.
problem Imbalanced and heterogeneous credit scoring datasets.
method Bagging Supervised Autoencoder Classifier (BSAC) that uses autoencoders and undersampling.
result BSAC improves classification of loan applicants, demonstrating robustness and effectiveness.
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…
In this paper we address the problem of enhancing speech signals in noisy mixtures using a source separation approach. We explore the use of neural networks as an alternative to a popular speech variance model based on supervised non-negative matrix factorization (NMF). More precisely, we use a variational autoencoder …
We present a new flavor of Variational Autoencoder (VAE) that interpolates seamlessly between unsupervised, semi-supervised and fully supervised learning domains. We show that unlabeled datapoints not only boost unsupervised tasks, but also the classification performance. Vice versa, every label not only improves class…
We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is …
We propose the Autoencoding Binary Classifiers (ABC), a novel supervised anomaly detector based on the Autoencoder (AE). There are two main approaches in anomaly detection: supervised and unsupervised. The supervised approach accurately detects the known anomalies included in training data, but it cannot detect the unk…
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.
Domain adaptation aims to exploit the knowledge in source domain to promote the learning tasks in target domain, which plays a critical role in real-world applications. Recently, lots of deep learning approaches based on autoencoders have achieved a significance performance in domain adaptation. However, most existing …
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.
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…
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…
Contrastive learning outperforms autoencoders and GANs in feature recovery and downstream tasks.
problem Theoretical understanding of contrastive learning's superiority in feature learning.
method Theoretical analysis of contrastive learning in linear representation settings.
result Contrastive learning outperforms autoencoders and GANs for feature recovery and in-domain downstream tasks.
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.
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.
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…
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.
In this paper we address speaker-independent multichannel speech enhancement in unknown noisy environments. Our work is based on a well-established multichannel local Gaussian modeling framework. We propose to use a neural network for modeling the speech spectro-temporal content. The parameters of this supervised model…
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.
We solve a high-dimensional model where nonlinear autoencoders detect hidden structure missed by PCA.
problem Hidden structure in high-dimensional data not detected by PCA.
method Tractable spiked model with two latent factors, one visible and one uncorrelated.
result Nonlinear autoencoders can extract hidden structure missed by PCA, even if reconstruction loss is higher.
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.
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.
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.
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.
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.
AugmentedPCA improves PCA with supervised or adversarial objectives.
problem Lack of reproducible linear analogs for deep latent factor models.
method Augments PCA with supervised or adversarial objectives.
result Improves downstream classification performance and identifies cancer-related genes.
The Information Plane theory predicts autoencoders do not compress input information.
problem Understanding the training dynamics of hidden layers in autoencoders.
method Derive a theoretical convergence for the Information Plane of autoencoders using a Gram-matrix based mutual information estimator.
result Ideal autoencoders with a large bottleneck layer size do not compress input information, while a small size causes compression only in the encoder layers.
We develop a novel probabilistic generative model based on the variational autoencoder approach. Notable aspects of our architecture are: a novel way of specifying the latent variables prior, and the introduction of an ordinality enforcing unit. We describe how to do supervised, unsupervised and semi-supervised learnin…
In this paper, we describe the "implicit autoencoder" (IAE), a generative autoencoder in which both the generative path and the recognition path are parametrized by implicit distributions. We use two generative adversarial networks to define the reconstruction and the regularization cost functions of the implicit autoe…
Centroid-Encoder reduces high-dimensional data for better visualization.
problem Visualizing high-dimensional data efficiently and accurately.
method Centroid-Encoder integrates label information to keep similar objects close in reduced space.
result Centroid-Encoder outperforms other techniques in visualizing high-dimensional data.
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.
Active learning improves soft sensor development by suggesting informative labels.
problem Expensive labeling of process variables in industrial settings.
method Adapted active learning strategies for online data streams and used semi-supervised autoencoders.
result Improved predictive performance of soft sensors using active learning and autoencoders.
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.
Autoencoders identify brain networks linked to stress and genotype.
problem Designing effective brain stimulation protocols for mental illnesses.
method Supervised autoencoders to model multi-region brain activity.
result Autoencoders reveal a stress-related brain network associated with a bipolar disorder genotype.
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.
New method disentangles hidden data structures using HSIC and supervision.
problem Tackles the challenge of interpreting high-dimensional data.
method Supervised Independent Subspace Principal Component Analysis (sisPCA) using HSIC.
result Identifies and separates hidden data structures effectively.
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.