Search Results - Denoising sequence autoencoder~

Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences by 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…”
Get full text

Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation by 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…”
Get full text

Deinterleaving of Pulse Streams With Denoising Autoencoders by 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…”
Get full text

Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders by 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…”
Get full text

PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions by 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…”
Get full text

Grammatical Error Correction with Denoising Autoencoder by 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…”
Get full text

Reconstruction of Power System Measurements Based on Enhanced Denoising Autoencoder by 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…”
Get full text

Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar by 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…”
Get full text

Signal Separation Method for Radiation Sources Based on a Parallel Denoising Autoencoder by 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…”
Get full text

An Approach for Predicting Protein-Protein Interactions using Supervised Autoencoders by Albu, Alexandra-Ioana

“… In this paper, we introduce a two-stage sequence-based PPI prediction method based on supervised autoencoders…”
Get full text

Denoising of Radar Pulse Streams With Autoencoders by 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…”
Get full text

Improving Machine Translation Quality with Denoising Autoencoder and Pre-Ordering by 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…”
Get full text

A transformer model blended with CNN and denoising autoencoder for inter-patient ECG arrhythmia classification by 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…”
Get full text

Single-cell RNA-seq denoising using a deep count autoencoder by 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…”
Get full text

Predicting protein–protein interactions in microbes associated with cardiovascular diseases using deep denoising autoencoders and evolutionary information by 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…”
Get full text

Short-Term Wind-Speed Forecasting Based on Multiscale Mathematical Morphological Decomposition, K-Means Clustering, and Stacked Denoising Autoencoders by 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…”
Get full text

Aircraft engines Remaining Useful Life prediction with an adaptive denoising online sequential Extreme Learning Machine by 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…”
Get full text

Financial Volatility Prediction Model Based On Denoising Autoencoder and Unstable Attention Mechanism by Fan, Yijiao

Get full text

miTarDigger: A Fusion Deep-learning Approach for Predicting Human miRNA Targets by 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…”
Get full text

A short‐term wind power prediction method based on deep learning and multistage ensemble algorithm by 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…”
Get full text