Results 1 to 10 of about 4,772,239 (326)
Optimizing Variational Graph Autoencoder for Community Detection with Dual Optimization
Entropy, 2020 Variational Graph Autoencoder (VGAE) has recently gained traction for learning representations on graphs. Its inception has allowed models to achieve state-of-the-art performance for challenging tasks such as link prediction, rating prediction, and node ...
Jun Jin Choong, Xin Liu, Tsuyoshi Murata
exaly +4 more sources
Dirichlet Variational Autoencoder [PDF]
Pattern Recognition, 2020 This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior for a continuous latent variable that exhibits the characteristic of the categorical probabilities. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the Gamma distribution, which is a component of the Dirichlet ...
Sungrae Park, Weonyoung Joo, Wonsung Lee
exaly +4 more sources
Hybrid variational autoencoder for time series forecasting [PDF]
Knowledge-Based Systems, 2023 Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables. Recent studies show that VAE can flexibly learn the complex temporal dynamics of time series and achieve more promising
Borui Cai, Longxiang Gao, Shuiqiao Yang
exaly +2 more sources
International Journal of Environmental Research and Public Health, 2023 A breast tissue biopsy is performed to identify the nature of a tumour, as it can be either cancerous or benign. The first implementations involved the use of machine learning algorithms.
Ajith Abraham +2 more
exaly +2 more sources
Generative autoencoder to prevent overregularization of variational autoencoder
ETRI JournalIn machine learning, data scarcity is a common problem, and generative models have the potential to solve it. The variational autoencoder is a generative model that performs variational inference to estimate a low-dimensional posterior dis-tribution ...
Youngmin Ko, Sunwoo Ko
exaly +2 more sources
Transformer graph variational autoencoder for generative molecular design. [PDF]
Biophys JIn the field of drug discovery, the generation of new molecules with desirable properties remains a critical challenge. Traditional methods often rely on SMILES (Simplified Molecular Input Line Entry System) representations for molecular input data ...
Nguyen T, Karolak A.
europepmc +2 more sources
Sensors, 2017 The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase
Belén Carro +2 more
exaly +3 more sources
Poisson Variational Autoencoder
Advances in Neural Information Processing Systems 37Variational autoencoders (VAEs) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral [1] and dorsal [2] pathways.
Hadi Vafaii, Dekel Galor, Jacob L. Yates
semanticscholar +6 more sources
AD-VAE: Adversarial Disentangling Variational Autoencoder [PDF]
SensorsFace recognition (FR) is a less intrusive biometrics technology with various applications, such as security, surveillance, and access control systems. FR remains challenging, especially when there is only a single image per person as a gallery dataset ...
Adson Silva, Ricardo Farias
doaj +2 more sources
Variational Autoencoder for the Prediction of Oil Contamination Temporal Evolution in Water Environments [PDF]
SensorsThe water quality monitoring of large water masses using robotic vehicles is a complex task highly developed in recent years. The main approaches utilize adaptative informative path planning of fleets of autonomous surface vehicles and computer learning ...
Alejandro Casado-Pérez +4 more
doaj +2 more sources