Soft-AE interprets autoencoders with adaptable wavelet units.
problem Lack of interpretability in autoencoders.
method Proposes Soft-AE with adaptable wavelet units and GenLU.
result Soft-AE offers interpretability and competitive performance.
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arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
169,051 papers · 148 categories
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48 results for “deep linear autoencoder”
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.
The paper analyzes -regularized linear autoencoders and their loss landscapes.
problem Understanding the loss landscapes of -regularized linear autoencoders.
method Smoothly parameterizing the critical manifold and relating minima to the MAP estimate of probabilistic PCA.
result Proves that -regularized LAEs learn principal directions as left singular vectors of the decoder.
Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dim…
The autoencoder is an effective unsupervised learning model which is widely used in deep learning. It is well known that an autoencoder with a single fully-connected hidden layer, a linear activation function and a squared error cost function trains weights that span the same subspace as the one spanned by the principa…
Simple linear model outperforms deep models on sparse data.
problem Sparse data in recommender systems.
method Linear autoencoder with closed-form solution.
result Simple model achieves better ranking accuracy than deep models.
This work analyzes how bottleneck layers and skip connections affect linear denoising autoencoders' generalization.
problem Understanding the generalization of linear denoising autoencoders in overparameterized regimes.
method Analyzes two-layer linear denoising autoencoders with a bottleneck layer and skip connection, deriving test risk formulas.
result Bottleneck layers introduce an additional complexity measure, while skip connections can mitigate variance.
This paper examines critical points in deep linear autoencoders.
problem Locating critical points in non-convex surfaces for optimization.
method Investigated two sub-problems: sampling and numerical tolerance.
result Numerical methods often fail to sample all critical points and require strict numerical tolerance.
Deep autoencoder finds linear PDE coordinates for nonlinear equations.
problem Discovering linear coordinates for nonlinear PDEs.
method Residual network architecture for finding intrinsic coordinates.
result Deep learning autoencoder transforms nonlinear PDEs into linear ones.
This paper proposes a novel model for the rating prediction task in recommender systems which significantly outperforms previous state-of-the art models on a time-split Netflix data set. Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training. We empirically de…
Variational autoencoders learn deep latent models.
problem Learning deep latent-variable models.
method Principled framework using variational inference.
result Introduction to variational autoencoders and extensions.
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.
Herein, we present a system for hyperspectral image segmentation that utilizes multiple class--based denoising autoencoders which are efficiently trained. Moreover, we present a novel hyperspectral data augmentation method for labelled HSI data using linear mixtures of pixels from each class, which helps the system wit…
ALIME proposes an autoencoder-based method for making deep learning models locally interpretable.
problem Making opaque deep learning models interpretable, especially in medical applications.
method Inspired by LIME, ALIME uses an autoencoder to generate a more stable and locally faithful explanation around a single instance.
result Our method improves both the stability and local fidelity of explanations compared to traditional LIME.
A new clustering method using autoencoders for improved data representation.
problem Improving clustering of complex data like images and text.
method DAMIC algorithm based on a mixture of deep autoencoders.
result Significant improvement over state-of-the-art methods on image and text corpora.
Study on dynamics of non-linear autoencoders learning principal components.
problem Technical difficulty in studying non-linear autoencoders due to non-trivial correlations.
method Derive asymptotically exact equations for SGD training of shallow, non-linear autoencoders.
result Autoencoders learn principal components sequentially and tie weights are ineffective.
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.
Probabilistic deep learning uses neural networks and models to handle uncertainty.
problem Handling uncertainty in deep learning models.
method Two approaches: probabilistic neural networks and deep probabilistic models.
result TensorFlow Probability library supports both approaches.
A new model combines spectral clustering and deep autoencoder for robust clustering.
problem Robustness issues in clustering approaches due to deep autoencoder hyperparameters.
method Ensemble Deep Autoencoder Learning (SC-EDAE) framework integrating spectral clustering and deep learning.
result Demonstrates improved robustness and performance compared to state-of-the-art methods.
DNI recovers missing brain data from corrupted recordings.
problem Corrupted neural recordings from multielectrode systems.
method Deep Neural Imputation framework using autoencoders.
result DNI recovers both time series and frequency content from corrupted data.
A new deep clustering model learns both clustering and embedding simultaneously.
problem Optimizing autoencoders for clustering and embedding separately.
method Integrating clustering module into a deep autoencoder.
result Joint learning of clustering and embedding improves performance.
A new method for deep clustering uses autoencoded embeddings and local manifold learning.
problem Improving clustering performance in deep learning models.
method Learning an autoencoded embedding, then clustering the underlying manifold using a shallow algorithm.
result UMAP is best at finding the most clusterable manifold in the embedding.
Bayesian deep learning faces posterior collapse due to likelihood vs. prior competition.
problem Posterior collapse in Bayesian deep learning models.
method Identified competition between likelihood and prior regularization in a linear latent variable model.
result Posterior collapse is related to neural and dimensional collapse, suggesting a broader learning issue.
CRsAE autoencoder learns dictionary for image denoising and brain spike detection.
problem Sparse dictionary learning for image and neural signal processing.
method CRsAE architecture, alternating E-step and M-step, FISTA for sparse coding, EM-inspired regularization.
result CRsAE outperforms conventional methods in image denoising and spike detection.
Paper proposes a deep subspace clustering method using multi-level representations.
problem Deep subspace clustering of images.
method Convolutional autoencoders with multiple fully-connected layers for multi-level representations, loss minimization with iterative updates.
result The method outperforms state-of-the-art methods on real-world datasets.
Linear VAEs explain posterior collapse in VAEs via local maxima in log marginal likelihood.
problem Posterior collapse in VAEs where variational posterior matches prior for some latent variables.
method Analysis of linear VAEs and their relation to pPCA, proving ELBO does not introduce spurious local maxima.
result Linear VAEs have identifiable global maxima corresponding to principal component directions, explaining posterior collapse.
New method uses deep learning for better monitoring of industrial processes.
problem Monitoring high-dimensional, nonlinear profiles in industrial systems.
method Variational Autoencoders (VAEs) for modeling nonlinear manifolds.
result Deep probabilistic models outperform traditional methods in residual-space.
DICCA maps multi-view data into a shared latent space with interpretable components.
problem Learning from multiple related but distinct data views.
method DICCA extends CCA to deep generative networks and uses sparsity-inducing priors for interpretability.
result DICCA effectively disentangles shared and view-specific variations in multi-view 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.
This work combines deep learning and sparse coding for CT image reconstruction.
problem Improving image quality in low-dose CT scans.
method Sparse signal representation using learned dictionaries, inspired by variational autoencoders and deep learning techniques.
result Regularization with learned dictionaries achieves competitive performance in CT reconstruction.
RAEUFS selects features from data without labels, improving robustness to outliers.
problem Feature selection in high-dimensional data, especially in the presence of outliers.
method RAEUFS uses a deep autoencoder to learn nonlinear feature representations, improving robustness to outliers.
result RAEUFS outperforms state-of-the-art UFS methods in both clean and outlier-contaminated data settings.
Proposes SDCN to integrate structural information into deep clustering.
problem Lack of attention to structural information in representation learning for clustering.
method Designs a delivery operator to transfer autoencoder representations to GCN layers and uses a dual self-supervised mechanism.
result SDCN consistently outperforms state-of-the-art techniques in clustering tasks.
In this paper, we develop a new framework for sensing and recovering structured signals. In contrast to compressive sensing (CS) systems that employ linear measurements, sparse representations, and computationally complex convex/greedy algorithms, we introduce a deep learning framework that supports both linear and mil…
Improves clustering performance by mixing latent representations.
problem Finding well-defined clusters in data representations.
method Mixing Consistent Deep Clustering method that encourages realistic interpolations and semantic consistency.
result Improved clustering performance across various models and datasets.
Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to …
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.
The ability of deep neural networks to generalize well in the overparameterized regime has become a subject of significant research interest. We show that overparameterized autoencoders exhibit memorization, a form of inductive bias that constrains the functions learned through the optimization process to concentrate a…
A cascaded autoencoder defends machine learning models from adversarial attacks.
problem Adversarial attacks on machine learning models.
method Denoising and dimensionality reduction using cascaded autoencoders.
result Preprocessed data with cascaded autoencoder pipeline improves model accuracy against adversarial perturbations.
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.
Langevin autoencoders improve deep latent variable models with efficient posterior sampling.
problem Efficient posterior sampling in deep latent variable models using MCMC.
method Amortized Langevin dynamics (ALD) replaces datapoint-wise sampling with encoder updates.
result ALD is valid as an MCMC algorithm with the target posterior as a stationary distribution.
Autoencoders improve unsupervised and semi-supervised learning with new generalization bounds.
problem Lack of theoretical understanding of autoencoder generalization in unsupervised and semi-supervised learning.
method Utilized recent advances in deep learning theory and a novel reconstruction loss to provide generalization bounds.
result First theoretical generalization bounds for autoencoders in unsupervised and semi-supervised learning.
LOCA learns standardized data coordinates from measurements.
problem Learning invariant data coordinates from non-linearly deformed manifolds.
method LOCA, a LOcal Conformal Autoencoder, learns an isometric embedding.
result LOCA preserves geometric information while learning invariant coordinates.
We introduce Embed to Control (E2C), a method for model learning and control of non-linear dynamical systems from raw pixel images. E2C consists of a deep generative model, belonging to the family of variational autoencoders, that learns to generate image trajectories from a latent space in which the dynamics is constr…
Proposes SOMDAGMM for more accurate network intrusion detection.
problem Inaccurate network intrusion detection in secure network environments.
method Integrates self-organizing map with deep autoencoding Gaussian mixture model.
result SOMDAGMM outperforms state-of-the-art DAGMM with up to 15.58% improvement in F1 score.
The paper analyzes the generalizability of linear autoencoders and multivariate linear regression.
problem Limited theoretical understanding of linear autoencoders' performance.
method Proposes a PAC-Bayes bound for multivariate linear regression and shows LAEs as constrained models.
result The proposed PAC-Bayes bound is tight and correlates with practical metrics.
Deep neural networks can store and recall data efficiently.
problem Identifying computational mechanisms for memorization and retrieval of data.
method Training overparameterized autoencoders and sequence encoders using standard optimization methods.
result Overparameterized autoencoders and sequence encoders store and recall data efficiently as attractors.
Autoencoder improves imputation across various missing data types.
problem Efficiently impute missing data in diverse datasets.
method Developed a deep autoencoder framework for consistent training and imputation.
result Autoencoder outperformed state-of-the-art methods in all experiments.
Study uses autoencoders to classify cryptocurrencies over 12 periods.
problem Classify cryptocurrencies based on market behavior.
method Deep convolutional autoencoder neural network.
result Cryptocurrencies transition between classes over time.