Výsledky vyhľadávania - "fully-connected autoencoder"

Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations Autor Xuan, Ping, Gao, Ling, Sheng, Nan, Zhang, Tiangang, Nakaguchi, Toshiya

“…Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods…”
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Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs Autor Xuan, Ping, Gong, Zhe, Cui, Hui, Li, Bochong, Zhang, Tiangang

“… We proposed a new method based on fully connected autoencoders and convolutional neural networks, called ACLDA, for inferring potential disease-related lncRNA candidates…”
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DSFC-AE: A New Hyperspectral Unmixing Method Based on Deep Shared Fully Connected Autoencoder Autor Chen, Hao, Chen, Tao, Zhang, Yuxiang, Du, Bo, Plaza, Antonio

“…The pervasive presence of mixed pixels in hyperspectral remote sensing imagery poses a substantial constraint on the quantitative progress of remote sensing…”
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Fully connected autoencoder and convolutional neural network with attention-based method for inferring disease-related lncRNAs Autor Ping, Xuan, Zhe, Gong, Hui, Cui, Bochong, Li, Tiangang, Zhang

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Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations Autor Ping Xuan, Ling Gao, Nan Sheng, Tiangang Zhang, Toshiya Nakaguchi

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EPTM: Explainable Parameter Tuning Method for Intelligent Modulation Recognition with Fully Connected Autoencoder Autor Li, Zerun, Wang, Yu, Xue, Yang, Liu, Xinqun

“…This paper introduces the current development for the technology of intelligent modulation recognition with fully connected autoencoder…”
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Prediction of Drug-Related Diseases Through Integrating Pairwise Attributes and Neighbor Topological Structures Autor Song, Yingying, Cui, Hui, Zhang, Tiangang, Yang, Tingxiao, Li, Xiaokun, Xuan, Ping

“…Identifying new disease indications for the approved drugs can help reduce the cost and time of drug development. Most of the recent methods focus on…”
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Surrogate Modeling of Hydrogen-Enriched Combustion Using Autoencoder-Based Dimensionality Reduction Autor Zhang, Lanfei, Chu, Xu, Ding, Siyu, Zhou, Mingshuo, Ni, Chenxu, Wang, Xingjian

“… (ROMs) and fully connected autoencoder (FCAE)-based ROMs, for emulating hydrogen-enriched combustion from a triple-coaxial nozzle jet…”
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A variational autoencoder trained with priors from canonical pathways increases the interpretability of transcriptome data Autor Liu, Bin, Rosenhahn, Bodo, Illig, Thomas, DeLuca, David S.

“…Interpreting transcriptome data is an important yet challenging aspect of bioinformatic analysis. While gene set enrichment analysis is a standard tool for…”
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A comparison of neural network architectures for data-driven reduced-order modeling Autor Gruber, Anthony, Gunzburger, Max, Ju, Lili, Wang, Zhu

“…: a simple fully connected autoencoder, and a novel graph convolutional autoencoder. Through benchmark experiments, it is shown that the superior autoencoder architecture…”
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Stacked pruning sparse denoising autoencoder based intelligent fault diagnosis of rolling bearings Autor Zhu, Haiping, Cheng, Jiaxin, Zhang, Cong, Wu, Jun, Shao, Xinyu

“… Different from the traditional autoencoder, the proposed sPSDAE model, including a fully connected autoencoder network, uses the superior features extracted in all the previous layers to participate…”
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Integrating multi-scale neighbouring topologies and cross-modal similarities for drug–protein interaction prediction Autor Xuan, Ping, Zhang, Yu, Cui, Hui, Zhang, Tiangang, Guo, Maozu, Nakaguchi, Toshiya

“… Then a learning framework based on fully-connected autoencoder is proposed to learn the nodes…”
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Learning Temporal Regularity in Video Sequences Autor Hasan, Mahmudul, Jonghyun Choi, Neumann, Jan, Roy-Chowdhury, Amit K., Davis, Larry S.

“… We first leverage the conventional handcrafted spatio-temporal local features and learn a fully connected autoencoder…”
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An explainable framework for partial discharge detection in power cables through the integration of rough set theory and deep learning Autor Shamsoddini, Morteza, Lan, Tongkun, Mazhari, Mahdi, Ko, Seok-Bum, Chung, Chi Yung

“… ••Deep features are extracted using a fully-connected autoencoder enhanced by morphological denoising…”
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Exploring Scalable, Distributed Real-Time Anomaly Detection for Bridge Health Monitoring Autor Moallemi, Amirhossein, Burrello, Alessio, Brunelli, Davide, Benini, Luca

“…), fully connected autoencoder (FC-AE), and convolutional autoencoder (C-AE). Then, we deploy them on an edge-sensor, the STM32L4…”
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On the neural network flow of spin configurations Autor Acevedo, Santiago, Lamas, Carlos A., Costa Duran, Alejo, Sturla, Mauricio B., Grigera, Tomás S.

“… Here we focus on the flow produced by a fully-connected autoencoder, and we refute the claim that this flow converges to the critical point of the system by directly measuring physical observables…”
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Visual exploration of latent space for traditional Chinese music Autor Shen, Jingyi, Wang, Runqi, Shen, Han-Wei

“…Generating compact and effective numerical representations of data is a fundamental step for many machine learning tasks. Traditionally, handcrafted features…”
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0062 Improved Circadian Data Ordering in the Presence of Biological and Technical Confounds Autor Hammarlund, J, Anafi, R

“… These layers feed into a fully connected autoencoder…”
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Attention-Empowered Residual Autoencoder for End-to-End Communication Systems Autor Lu, Min, Zhou, Bin, Bu, Zhiyong

“… The existing deep fully connected autoencoder needs retraining when the input length of bit sequences changes since it can only handle fixed-length data…”
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Enhancing Binary Change Detection in Hyperspectral Images Using an Efficient Dimensionality Reduction Technique Within Adversarial Learning Autor Oubara, Amel, Wu, Falin, Qu, Guoxin, Maleki, Reza, Yang, Gongliu

“…Detecting binary changes in co-registered bitemporal hyperspectral images (HSIs) using deep learning methods is challenging due to the high dimensionality of…”
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