Suchergebnisse - deep convolutional autoencoder (ConvAE)

Impartially Validated Multiple Deep-Chain Models to Detect COVID-19 in Chest X-ray Using Latent Space Radiomics von Yousefi, Bardia, Kawakita, Satoru, Amini, Arya, Akbari, Hamed, Advani, Shailesh M., Akhloufi, Moulay, Maldague, Xavier P. V., Ahadian, Samad

“… First, we used a 2D U-Net model to segment the lung lobes. Then, we extracted deep latent space radiomics by applying deep convolutional autoencoder (ConvAE …”
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ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition von Thakur, Dipanwita, Biswas, Suparna, Ho, Edmond S. L., Chattopadhyay, Samiran

“… , accelerometer and gyroscope data. Convolutional neural networks (CNNs), autoencoders (AEs), and long short-term memory (LSTM …”
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ConvAE: A New Channel Autoencoder Based on Convolutional Layers and Residual Connections von Ji, Dong Jin, Park, Jinsol, Cho, Dong-Ho

“… In this letter, we propose ConvAE, a new channel autoencoder structure. ConvAE uses residual blocks with convolutional layers …”
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Interpretable regional meteorological feature extraction enhances deep learning for extended 120-h PM2.5 forecasting von Liu, Xinyi, Pu, Xueting, Lu, Chengwei, Zhang, Han, Li, Tao, Grieneisen, Michael L., Li, Jucheng, Ma, Ning, Yan, Chang, Zhan, Yu, Yang, Fumo

“… We used convolutional autoencoders (ConvAEs) to extract regional meteorological features (RMFs …”
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Anomaly Detection in Videos Using Optical Flow and Convolutional Autoencoder von Duman, Elvan, Erdem, Osman Ayhan

“… In this paper, we propose a framework (OF-ConvAE-LSTM) to detect anomalies using Convolutional Autoencoder and Convolutional Long Short-Term Memory in an unsupervised manner …”
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An Application of Deep Learning Technique to Improve Subseasonal to Seasonal Rainfall Forecast over Java Island, Indonesia von Raharja, Adyaksa Budi, Faqih, Akhmad, Setiawan, Amsari Mudzakir

“… However, the forecast remains challenging due to its lack of skill. This study applies Convolutional AutoEncoders (ConvAE …”
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Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models von Kow, Pu-Yun, Liou, Jia-Yi, Sun, Wei, Chang, Li-Chiu, Chang, Fi-John

“… This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE …”
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Latent space representation of electronic health records for clustering dialysis-associated kidney failure subtypes von Onthoni, Djeane Debora, Lin, Ming-Yen, Lan, Kuei-Yuan, Huang, Tsung-Hsien, Lin, Hong-Ming, Chiou, Hung-Yi, Hsu, Chih-Cheng, Chung, Ren-Hua

“… This matrix structure was achieved using a unique data cutting method. Latent space transformation was facilitated using a convolution autoencoder (ConvAE …”
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Unsupervised anomaly detection for pome fruit quality inspection using X-ray radiography von Tempelaere, Astrid, He, Jiaqi, Van Doorselaer, Leen, Verboven, Pieter, Nicolai, Bart, Valerio Giuffrida, Mario

“… •Our model outperforms the traditional autoencoder architecture. A novel fully convolutional autoencoder (convAE …”
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Deep representation learning of electronic health records to unlock patient stratification at scale von Landi, Isotta, Glicksberg, Benjamin S., Lee, Hao-Chih, Cherng, Sarah, Landi, Giulia, Danieletto, Matteo, Dudley, Joel T., Furlanello, Cesare, Miotto, Riccardo

“… We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE …”
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LCF: A Deep Learning-Based Lightweight CSI Feedback Scheme for MIMO Networks von Lee, Kyu-haeng

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Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights von Wu, Shih-Lun, Tung, Hsiao-Yen, Hsu, Yu-Lun

“… ; and, a family of self-defined convolutional autoencoder-classifiers (ConvAE-Clfs) inspired by the claimed benefit of multi-task learning in classification tasks …”
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Deep Crowd Anomaly Detection by Fusing Reconstruction and Prediction Networks von Sharif, Md. Haidar, Jiao, Lei, Omlin, Christian W.

“… Many existing deep anomaly detection models are based on reconstruction errors, where the training phase is performed using only videos of normal events and the model is then capable to estimate …”
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Deep representation learning of electronic health records to unlock patient stratification at scale von Landi, Isotta, Glicksberg, Benjamin S., Lee, Hao-Chih, Cherng, Sarah, Landi, Giulia, Danieletto, Matteo, Dudley, Joel T., Furlanello, Cesare, Miotto, Riccardo

“… We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE …”
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Rapid prediction of grain boundary network evolution in nanomaterials utilizing a generative machine learning approach von Wang, Yuheng, Kazemi, Amirreza, Jing, Taotao, Ding, Zhengming, Li, Like, Yang, Shengfeng

“… ) models have opened new avenues for the rapid exploration of design spaces. In this work, we developed a deep learning framework based on a conditional generative adversarial network (cGAN …”
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Deep Representation Learning of Electronic Health Records to Unlock Patient Stratification at Scale von Landi, Isotta, Glicksberg, Benjamin S, Hao-Chih, Lee, Cherng, Sarah, Landi, Giulia, Danieletto, Matteo, Dudley, Joel T, Furlanello, Cesare, Miotto, Riccardo

“… We introduce a representation learning model based on word embeddings, convolutional neural networks, and autoencoders (i.e., ConvAE …”
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Deep Learning for Automatic Quality Grading of Mangoes: Methods and Insights von Shih-Lun, Wu, Hsiao-Yen, Tung, Yu-Lun, Hsu

“… ; and, a family of self-defined convolutional autoencoder-classifiers (ConvAE-Clfs) inspired by the claimed benefit of multi-task learning in classification tasks …”
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