Results 51 to 60 of about 75,818 (178)
Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 Anomaly detection on attributed networks aims at finding nodes whose patterns deviate significantly from the majority of reference nodes, which is pervasive in many applications such as network intrusion detection and social spammer detection.
Haoyi Fan, Fengbin Zhang, Zuoyong Li
semanticscholar +1 more source
Auto-Encoders Derivatives on Different Occluded Face Images: Comprehensive Review and New Results
IEEE AccessThis paper presents a novel approach for improving occluded face recognition performance using a family of autoencoders (AE) architectures. The proposed structures include four stages: image preprocessing, feature extraction using autoencoder derivatives,
Azin Masoudi, Majid Ahmadi
doaj +1 more source
Anomaly Detection for Sensor Signals Utilizing Deep Learning Autoencoder-Based Neural Networks
Bioengineering, 2023 Anomaly detection is a significant task in sensors’ signal processing since interpreting an abnormal signal can lead to making a high-risk decision in terms of sensors’ applications.
Fatemeh Esmaeili +5 more
semanticscholar +1 more source
Adversarially Regularized Graph Autoencoder [PDF]
International Joint Conference on Artificial Intelligence, 2018 Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph ...
Shirui Pan +5 more
semanticscholar +1 more source
Multiresolution convolutional autoencoders
Journal of Computational Physics, 2023 20 pages, 11 ...
Yuying Liu +3 more
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An Anomaly Detection Method for UAV Based on Wavelet Decomposition and Stacked Denoising Autoencoder
AerospaceThe paper proposes an anomaly detection method for UAVs based on wavelet decomposition and stacked denoising autoencoder. This method takes the negative impact of noisy data and the feature extraction capabilities of deep learning models into account. It
Shenghan Zhou +3 more
semanticscholar +1 more source
Quantum variational autoencoder [PDF]
Quantum Science and Technology, 2018 Variational autoencoders (VAEs) are powerful generative models with the salient ability to perform inference. Here, we introduce a quantum variational autoencoder (QVAE): a VAE whose latent generative process is implemented as a quantum Boltzmann machine (QBM). We show that our model can be trained end-to-end by maximizing a well-defined loss-function:
Amir Khoshaman +5 more
openaire +2 more sources
Symmetric Wasserstein Autoencoders
, 2021 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, July 27-30, 2021, Virtual ...
Sun, Sun, Guo, Hongyu
openaire +3 more sources
Autoencoding Topographic Factors [PDF]
Journal of Computational Biology, 2019 Topographic factor models separate overlapping signals into latent spatial functions to identify correlation structure across observations. These methods require the underlying structure to be held fixed and are not robust to deviations commonly found across images.
Antonio, Moretti +3 more
openaire +2 more sources
Remote SensingHigh-resolution satellite images are frequently used to measure horizontal displacements caused by earthquakes, providing valuable insights into rupture behaviors and mechanical properties of seismogenic faults.
Chenglong Li +4 more
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