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.
On-device research index
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.
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48 results for “Deep Autoencoder”
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.
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.
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.
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.
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.
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.
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.
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…
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…
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.
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.
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.
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.
Autoencoder identifies cancer cells from normal ones using gene expression data.
problem Distinguishing between normal and cancer cells using gene expression profiles.
method Autoencoder trained on large tumor dataset to capture latent representations, using HPC toolkit for efficiency.
result Autoencoder node saliency identifies key differentiating features between normal and cancer cells.
To train an inference network jointly with a deep generative topic model, making it both scalable to big corpora and fast in out-of-sample prediction, we develop Weibull hybrid autoencoding inference (WHAI) for deep latent Dirichlet allocation, which infers posterior samples via a hybrid of stochastic-gradient MCMC and…
We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into…
A DL autoencoder tackles interference channels, improving SNR and INR.
problem Improving performance in interference channels with varying interference levels.
method Designing a DL neural network autoencoder for a k-user Gaussian interference channel, classifying interferences as weak to very strong.
result DL autoencoder significantly mitigates interference effects, especially with known α and low offset.
Deep autoencoders improve health tweet clustering.
problem Accurate clustering of health-related tweets.
method Deep convolutional autoencoders for learning compact tweet representations.
result Clustering performance significantly outperforms conventional methods.
ADEC addresses feature randomness and drift in autoencoder-based clustering.
problem Clustering autoencoders learn unreliable pseudo-labels, distorting latent space and feature randomness.
method Adversarial training to balance reconstruction loss and clustering objective.
result ADEC outperforms state-of-the-art autoencoder-based clustering methods.
Pre-training deep RF classifiers with autoencoders mitigates adversarial attacks.
problem Adversarial examples in RF deep learning cause targeted misclassifications.
method Pre-training the classifier using an autoencoder.
result Pre-training method validates as a viable mitigation against adversarial attacks.
This work proposes a new method to train models with deep latent hierarchies using Optimal Transport.
problem Training models with deep latent hierarchies using VAEs often leads to the 'latent variable collapse' issue.
method Proposes a novel approach based on Optimal Transport to train models with deep latent hierarchies.
result The method avoids the 'latent variable collapse' issue and provides better sample generations and latent representation.
Study introduces UEBA framework using Deep Autoencoders for anomaly detection.
problem Detecting security incidents in cybersecurity.
method Combines Deep Autoencoders with Doc2Vec for anomaly detection.
result Demonstrates effective detection of real and synthetic anomalies.
A deep learning autoencoder improves error correction for one-bit quantization.
problem Improving error correction for one-bit quantization in AWGN channels.
method Proposes a novel autoencoder-based coding scheme using turbo codes as implicit regularization.
result Empirically and theoretically shows nearly optimal performance of the proposed coding scheme.
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…
Deep Kernelized Autoencodersstat.ML
In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional a…
Deep learning improves sparse representation for better classification.
problem Improving classification accuracy using sparse representation.
method A transductive deep learning network combining convolutional autoencoder and fully-connected layers.
result The proposed network achieves better classification results than state-of-the-art SRC methods.
Deep-SITAR uses autoencoders to predict growth patterns.
problem Predicting individual growth trajectories from population data.
method Deep learning framework integrating autoencoders and B-spline models.
result Deep-SITAR predicts individual growth without full model re-estimation.
A new clustering method using deep autoencoder networks and spectral clustering.
problem Improving clustering accuracy in noisy data.
method Dual autoencoder network for robust latent representations, mutual information estimation for discriminative features, deep spectral clustering.
result Significantly outperforms state-of-the-art clustering approaches on benchmark datasets.
Customer temporal behavioral data was represented as images in order to perform churn prediction by leveraging deep learning architectures prominent in image classification. Supervised learning was performed on labeled data of over 6 million customers using deep convolutional neural networks, which achieved an AUC of 0…
Survey of multimodal deep generative models for diverse data types.
problem Inference of shared representations and cross-modal generation from heterogeneous multimodal data.
method Variational autoencoders and other deep generative models.
result A comprehensive survey of multimodal deep generative models.
Currently there are two predominant ways to train deep neural networks. The first one uses restricted Boltzmann machine (RBM) and the second one autoencoders. RBMs are stacked in layers to form deep belief network (DBN); the final representation layer is attached to the target to complete the deep neural network. Autoe…
TES-AE uses tree grammars to speed up autoencoding for tree data.
problem Challenges in autoencoding tree data due to its non-vectorial and discrete nature.
method TES-AE combines reservoir computing with tree grammars for faster training.
result TES-AE outperforms D-VAE in speed and accuracy for tree data.
New method denoises and fills in missing image data without clean training data.
problem Denoising and inpainting images with noisy, incomplete data.
method Robust Hadamard Autoencoders trained on noisy, incomplete data.
result Autoencoders can perform denoising and inpainting simultaneously.
Detects adversarial examples using autoencoders at hidden layers.
problem Identifying adversarial examples in deep neural networks.
method Trains autoencoders on intermediate layers to detect deviations from true data.
result Outperforms state-of-the-art methods in both supervised and unsupervised settings.
Deep learning autoencoder detects bee colony anomalies.
problem Early detection of bee swarms and other unusual behaviors.
method Deep Recurrent Autoencoder model trained on sensor data.
result Autoencoder detects anomalies independent of their origin.
New method uses random convex polytopes to measure representation quality.
problem Measuring the quality of deep learning representations.
method Random Polytope Descriptor method based on random convex polytopes.
result Regularization in autoencoders can degrade out-of-distribution detection.
Paper uses RL and DCAE to classify large unstructured data with fewer features.
problem Classifying large unstructured data with high precision using fewer features.
method Deep Convolutional Autoencoder (DCAE) for feature learning and Double DQN/Retrace RL algorithms for policy optimization.
result The approach achieves high classification precision with fewer features than traditional methods.
Autoencoding topologystat.ML
The problem of learning a manifold structure on a dataset is framed in terms of a generative model, to which we use ideas behind autoencoders (namely adversarial/Wasserstein autoencoders) to fit deep neural networks. From a machine learning perspective, the resulting structure, an atlas of a manifold, may be viewed as …
Quadratic autoencoder improves low-dose CT image denoising.
problem Low-dose CT image denoising.
method Quadratic autoencoder architecture applied to CT denoising.
result Quadratic autoencoder achieves superior denoising performance and efficiency.
Tiered graph autoencoders improve molecular graph representation.
problem Representing and utilizing groups in molecular graphs.
method Adapting tiered graph autoencoders for PyTorch Geometric.
result Molecular graphs have tiered latent representations.
In this paper we describe the problem of painter classification, and propose a novel approach based on deep convolutional autoencoder neural networks. While previous approaches relied on image processing and manual feature extraction from paintings, our approach operates on the raw pixel level, without any preprocessin…
Deep neural network for cancer classification using autoencoders.
problem Cancer classification using molecular information.
method Using a Denoising Autoencoder (DAE) as weight initialization for a deep neural network, comparing two approaches: fixed weights and fine-tuning. Embedding strategies included encoding layers and complete autoencoder.
result Best F1 score of 98.04% for identifying thyroid cancer samples.