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Comprehensive study of variational Bayes classification for dense deep neural networks
Although Bayesian deep neural network models are ubiquitous in classification problems; their Markov Chain Monte Carlo based implementation suffers...
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Transformer-based Denoising Adversarial Variational Entity Resolution
Entity resolution (ER), precisely identifying different representations of the same real-world entities, is critical for data integration. The ER...
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Characterization of Brain Activity Patterns Across States of Consciousness Based on Variational Auto-Encoders
Decoding the levels of consciousness from cortical activity recording is a major challenge in neuroscience. The spontaneous fluctuations of brain...
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VAE-GNA: a variational autoencoder with Gaussian neurons in the latent space and attention mechanisms
Variational autoencoders (VAEs) are generative models known for learning compact and continuous latent representations of data. While they have...
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Madelung Transform and Variational Asymptotics in Born-Oppenheimer Molecular Dynamics
While Born-Oppenheimer molecular dynamics (BOMD) has been widely studied by resorting to powerful methods in mathematical analysis, this paper...
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The computational asymptotics of Gaussian variational inference and the Laplace approximation
Gaussian variational inference and the Laplace approximation are popular alternatives to Markov chain Monte Carlo that formulate Bayesian posterior...
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A One-Class Variational Autoencoder (OCVAE) Cascade for Classifying Atypical Bone Marrow Cell Sub-types
Atypical bone marrow (BM) cell-subtype characterization defines the diagnosis and follow up of different hematologic disorders. However, this process...
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A Variational Algorithm for Quantum Single Layer Perceptron
Hybrid quantum-classical computation represents one of the most promising approaches to deliver novel machine learning models capable of overcoming...
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Iterative methods for solving monotone variational inclusions without prior knowledge of the Lipschitz constant of the single-valued operator
In this work, we investigate a contraction-type method for solving monotone variational inclusion problems in real Hilbert spaces. We obtain strong...
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Hypergraph Variational Autoencoder for Multimodal Semi-supervised Representation Learning
In many real-world settings, the external environment is perceived through multi-modal information, such as visual, radar, lidar, etc. Naturally, the...
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Robust Colorectal Polyp Characterization Using a Hybrid Bayesian Neural Network
Computer-Aided Diagnosis (CADx) systems can play a crucial role as a second opinion for endoscopists to improve the overall optical diagnostic...
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A Two-Stage Variational Inequality for Medical Supply in Emergency Management
In this paper, we study the competition of healthcare institutions for medical supplies in emergencies caused by natural disasters. In particular, we...
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Mixture of experts with convolutional and variational autoencoders for anomaly detection
This study focused on the problem of anomaly detection (AD) by means of mixture-of-experts network. Most of the existing AD methods solely based on...
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FlowSpectrum: a concrete characterization scheme of network traffic behavior for anomaly detection
As the 5G rolls out around the world, many edge applications will be deployed by app vendors and accessed by massive end-users. Efficient detection...
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Dictionary adaptation and variational mode decomposition for gyroscope signal enhancement
The paper proposes an approach to signal denoising based on a combination of Variational Mode Decomposition with the Split Augmented Lagrangian...
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A Combined Wavelet and Variational Mode Decomposition Approach for Denoising Texture Images
Edges and textures are important features in texture analysis that helps to characterize an image. Thus, the edges and textures must be retained...
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Joint-product representation learning for domain generalization in classification and regression
In this work, we study the problem of generalizing a prediction (classification or regression) model trained on a set of source domains to an unseen...
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Convex Object(s) Characterization and Segmentation Using Level Set Function
In practice, the objects of interest have some shape priors, which would be destroyed by occlusions, distortions and noises. Therefore, the...
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Towards a Characterization of Worst Case Equilibria in the Discriminatory Price Auction
We study the performance of the discriminatory price auction under the uniform bidding interface, which is one of the popular formats for running...
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A Variational Generative Network Based Network Threat Situation Assessment
In recent years, with the problem of network security is getting worse, the network threat situation assessment becomes an important approach to...