Results 61 to 70 of about 13,735 (297)
Genomic Data Augmentation with Variational Autoencoder [PDF]
, 2023 In order to treat cancer effectively, medical practitioners must predict pathological stages accurately, and machine learning methods can be employed to make such predictions.
Thyrum, Emily
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Business Process Anomaly Detection and Root Cause Analysis Using BLSTM-VAE With Attention
IEEE AccessDetecting anomalous executions in business process data is crucial for safeguarding the efficiency and success of an organization. Unsupervised approaches are commonly used for business process anomaly detection because of the scarcity of labeled anomaly
Eman Abd El-Aziz +3 more
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Adversarial Attention-Based Variational Graph Autoencoder
IEEE Access, 2020 Autoencoders have been successfully used for graph embedding, and many variants have been proven to effectively express graph data and conduct graph analysis in low-dimensional space.
Ziqiang Weng, Weiyu Zhang, Wei Dou
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CoRR, 2016 Representation learning seeks to expose certain aspects of observed data in a learned representation that's amenable to downstream tasks like classification. For instance, a good representation for 2D images might be one that describes only global structure and discards information about detailed texture.
Xi Chen 0022 +7 more
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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
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Variational Composite Autoencoders
CoRR, 2018 Learning in the latent variable model is challenging in the presence of the complex data structure or the intractable latent variable. Previous variational autoencoders can be low effective due to the straightforward encoder-decoder structure. In this paper, we propose a variational composite autoencoder to sidestep this issue by amortizing on top of ...
Jiangchao Yao +2 more
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Variational Autoencoder and its Extensions [PDF]
, 2020 Variační autoencoder je modelem, který v sobě kombinuje pravděpodobnostní přístup a sílu aproximací pomocí neuronových sítí. Lze jej využít jako generativní model nebo například v detekci anomálií. Jeho výhody i nevýhody, stejně jako jeho možná rozšíření,
Michaela Mašková
core
Unsupervised Anomaly Video Detection via a Double-Flow ConvLSTM Variational Autoencoder
IEEE Access, 2022 With the rapid increase of video surveillance points in the market in recent years, video anomaly detection has gained extensive attention in the security field.
Lin Wang +4 more
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IEEE AccessThis article presents a new regularization of the latent space of a variational autoencoder that facilitates the visual selection of specific emotions of generated monophonic musical sequences.
Jacek Grekow
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Anomaly Detection in Asset Degradation Process Using Variational Autoencoder and Explanations
Sensors, 2021 Development of predictive maintenance (PdM) solutions is one of the key aspects of Industry 4.0. In recent years, more attention has been paid to data-driven techniques, which use machine learning to monitor the health of an industrial asset.
Jakub Jakubowski +3 more
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