Suchergebnisse - Denoising sequence autoencoder

Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences von García-González, Jorge, Ortiz-de-Lazcano-Lobato, Juan M., Luque-Baena, Rafael M., Molina-Cabello, Miguel A., López-Rubio, Ezequiel

“… •Stacked Denoising Autoencoders are used to filter noise in video sequences.•The scene background is modeled by a probabilistic mixture of Gaussians of the features discovered by SDA …”
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Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation von Grönroos, Stig-Arne, Virpioja, Sami, Kurimo, Mikko

“… There are several approaches for improving neural machine translation for low-resource languages: monolingual data can be exploited via pretraining or data …”
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Deinterleaving of Pulse Streams With Denoising Autoencoders von Li, Xueqiong, Liu, Zhangmeng, Huang, Zhitao

“… ; their performance degrades in noisy environments. A novel approach based on denoising autoencoders for TOA deinterleaving was developed in this article …”
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Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders von Stanton, Samuel, Maddox, Wesley, Gruver, Nate, Maffettone, Phillip, Delaney, Emily, Greenside, Peyton, Andrew Gordon Wilson

“… We develop a new approach (LaMBO) which jointly trains a denoising autoencoder with a discriminative multi-task Gaussian process head, allowing gradient-based …”
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PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions von Zhou, Haoran, Wekesa, Jael Sanyanda, Luan, Yushi, Meng, Jun

“… Results In this study, we propose an ensemble deep learning model to predict plant lncRNA-protein interactions using stacked denoising autoencoder and convolutional neural network based on sequence …”
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Grammatical Error Correction with Denoising Autoencoder von Pajak, Krzysztof, Gonczarek, Adam

“… A denoising autoencoder sequence-to-sequence model based on transformer architecture proved to be useful for underlying tasks such as summarization, machine translation, or question answering …”
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Reconstruction of Power System Measurements Based on Enhanced Denoising Autoencoder von Lin, You, Wang, Jianhui, Cui, Mingjian

“… An Enhanced Denoising Autoencoder (EDAE) is proposed to reconstruct the missing data through the input vector space reconstruction based on the neighbor values correlation and Long Short-Term Memory (LSTM) networks …”
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Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar von Pegoraro, Jacopo, Solimini, Domenico, Matteo, Federico, Bashirov, Enver, Meneghello, Francesca, Rossi, Michele

“… In this work, we propose an original model-free tracking procedure based on denoising autoencoders and sequence-to-sequence neural networks, showing its superior performance with respect to state-of-the-art methods …”
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Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder von Tang, Xusheng, Wei, Mingfeng

“… This method implements the binarized preprocessing of known time-of-arrival (TOA) sequences and then constructs multiple parallel denoising autoencoder models using fully …”
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An Approach for Predicting Protein-Protein Interactions using Supervised Autoencoders von Albu, Alexandra-Ioana

“… In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders …”
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Denoising of Radar Pulse Streams With Autoencoders von Li, Xueqiong, Liu, Zhang-Meng, Huang, Zhitao

“… ) sequences using the autoencoders. The noise-contaminated TOA sequence is first coded into a binary vector and then fed into an autoencoder for training …”
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Improving Machine Translation Quality with Denoising Autoencoder and Pre-Ordering von Hong-Viet, Tran, Van-Vinh, Nguyen, Hoang-Quan, Nguyen

“… The problems in machine translation are related to the characteristics of a family of languages, especially syntactic divergences between languages. In the …”
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A transformer model blended with CNN and denoising autoencoder for inter-patient ECG arrhythmia classification von Xia, Yong, Xiong, Yueqi, Wang, Kuanquan

“… Heartbeats can enhance their feature representation by attending to adjacent heartbeats.•We provide a denoising autoencoder with multiple regularizations for local …”
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Single-cell RNA-seq denoising using a deep count autoencoder von Eraslan, Gökcen, Simon, Lukas M., Mircea, Maria, Mueller, Nikola S., Theis, Fabian J.

“… However, noise due to amplification and dropout may obstruct analyses, so scalable denoising methods for increasingly large but sparse scRNA-seq data are needed …”
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Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information von Zhou, Senyu, Luo, Jian, Tang, Mei, Li, Chaojun, Li, Yang, He, Wenhua

“… ), which leverages the denoising autoencoder and the CatBoost algorithm to predict PPIs from the evolutionary information of protein sequences …”
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Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders von Dong, Weichao, Sun, Hexu, Li, Zheng, Zhang, Jingxuan, Yang, Huifang

“… ), K-means clustering algorithm, and stacked denoising autoencoder (SDAE) networks. First, in contrast to traditional signal-decomposing tools, the original wind …”
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Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine von Berghout, Tarek, Mouss, Leïla-Hayet, Kadri, Ouahab, Saïdi, Lotfi, Benbouzid, Mohamed

“… In this paper, a new Denoising Online Sequential Extreme Learning Machine (DOS-ELM) with double dynamic forgetting factors (DDFF …”
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Financial Volatility Prediction Model Based On Denoising Autoencoder and Unstable Attention Mechanism von Fan, Yijiao

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miTarDigger: A Fusion Deep-learning Approach for Predicting Human miRNA Targets von Yan, Jianrong, Li, Yanan, Zhu, Min

“… s. In this study, a deep learning approach based on fusion of stacked denoising autoencoders (SDA) and Convolutional denoising autoencoders …”
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A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm von Peng, Xiaosheng, Li, Cong, Jia, Shiyuan, Zhou, Liangsong, Wang, Bo, Che, Jianfeng

“… In the second stage, based on the decomposition sequences, the stacked denoising autoencoder (SDAE), long short‐term memory (LSTM), and bidirectional long short …”
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