Suchergebnisse - sparse denoise autoencoder

Multistage committees of deep feedforward convolutional sparse denoise autoencoder for object recognition von Shicao Luo, Yongsheng Ding, Kuangrong Hao

“… The network is trained layer-wise via denoise autoencoder (dA) with L-BFGS to optimize convolutional kernels and no backpropagation is used …”
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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.

“… We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account …”
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Anomaly MFL Signal Recovery based on Denoising Sparse Autoencoder von Jiang, Lin, Liu, Jinhai, Shen, Xiangkai, Liu, Jiarui, Liu, Xiaoyuan, Zhang, Baojin, Xu, Hang

“… To overcome this problem, this paper proposes a novel anomaly MFL signal recovery method based on denoise sparse autoencoder (DSAE …”
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An Anomaly Detection Method for Nonlinear Industrial Process Using Sparse Stacked Denoising Autoencoder von Yang, Mingwei, Liu, YanHua, Chen, Hong, Lin, Jiefei, Lin, Haoqiang

“… Therefore, a sparse stacked denoise autoencoder(SSDAE) based anomaly detection model is proposed in the paper, which uses the autoencoder model to capture the nonlinear …”
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Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images von Xing, Chen, Yang, Xiaoquan, Ma, Li

“… We utilized stacked denoise autoencoder (SDAE) method to pretrain the network, which is robust to noise …”
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Hyperspectral image unmixing using autoencoder cascade von Rui Guo, Wei Wang, Hairong Qi

“… The proposed autoencoder cascade concatenates a marginalized denoising autoencoder and a non-negative sparse autoencoder to solve …”
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Flatness pattern recognition based on stacked sparse denoising autoencoder and improved Osprey optimisation algorithm kernel-extreme learning machine von Zhou, Yaluo, Zhang, Shaochuan, Liu, Wenguang, Zhang, Ruicheng

“… proposes a flatness recognition method based on stack sparse denoising autoencoder (SSDAE) with improved Osprey optimisation algorithm kernel-extreme learning machine …”
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Blind Denoising Autoencoder von Majumdar, Angshul

“… But there has been no autoencoder-based solution for the said blind denoising approach. So far, autoencoder-based denoising formulations have learned the model on a separate training data and have used the learned model to denoise test samples …”
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Multi-lead model-based ECG signal denoising by guided filter von Hao, Huaqing, Liu, Ming, Xiong, Peng, Du, Haiman, Zhang, Hong, Lin, Feng, Hou, Zengguang, Liu, Xiuling

“… For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE …”
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Microscopic segmentation and classification of COVID‐19 infection with ensemble convolutional neural network von Amin, Javeria, Anjum, Muhammad Almas, Sharif, Muhammad, Rehman, Amjad, Saba, Tanzila, Zahra, Rida

“… In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE …”
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Denoising magnetic resonance spectroscopy (MRS) data using stacked autoencoder for improving signal‐to‐noise ratio and speed of MRS von Wang, Jing, Ji, Bing, Lei, Yang, Liu, Tian, Mao, Hui, Yang, Xiaofeng

“… ‐learning approaches to denoise MRS data without increasing NSA. This method has potential to reduce the acquisition time as well as improve SNR and quality of spectra …”
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Network Intrusion Detection Based on Sparse Autoencoder and IGA-BP Network von Deng, Hongli, Yang, Tao

“… ) network is constructed. In order to reduce the data dimension and eliminate redundant information, the autoencoder network model is firstly used to denoise and dedimension …”
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scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering von Zhang, Tianjiao, Ren, Jixiang, Li, Liangyu, Wu, Zhenao, Zhang, Ziheng, Dong, Guanghui, Wang, Guohua

“… Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data …”
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DSCD: A Novel Deep Subspace Clustering Denoise Network for Single-Cell Clustering von Wang, Zhiye, Lu, Yiwen, Yu, Chang, Zhou, Tao, Li, Ruiyi, Hou, Siyun

“… Single-cell RNA sequencing(scRNA-seq) technology has boomed in the past decade which makes it possible to study biological problems at the resolution of …”
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Single cell RNA-seq denoising using a deep count autoencoder von G kcen Eraslan, Simon, Lukas M, Mircea, Maria, Mueller, Nikola S, Theis, Fabian J

“… We propose a deep count autoencoder network (DCA) to denoise scRNA-seq datasets. DCA takes the count distribution, overdispersion and sparsity of the data into account …”
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LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement von Lore, Kin Gwn, Adedotun Akintayo, Sarkar, Soumik

“… We propose a deep autoencoder-based approach to identify signal features from low-light images handcrafting and adaptively brighten images without over-amplifying the lighter parts in images (i.e …”
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Benchmarking Statistical and Machine-Learning Methods for Single-Cell RNA Sequencing Data von Xi, Nan

“… The large-scale, high-dimensional, and sparse single-cell RNA sequencing (scRNA-seq) data have raised great challenges in the pipeline of data analysis …”
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Stock Selection via Expand-excite Conv Attention Autoencoder and Layer Sparse Attention Transformer: A Classification Approach Inspire Time Series Sequence Recognition von Fu, Wentao, Sun, Jifeng, Jiang, Yong

“… In the past, we usually used feature engineering to denoise the original stock data. However, with the advent of Deep Learning, neural networks can now automatically perform feature engineering …”
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Single-Cell Transcriptome Data Clustering via Multinomial Modeling and Adaptive Fuzzy K-Means Algorithm von Chen, Liang, Wang, Weinan, Zhai, Yuyao, Deng, Minghua

“… Although a unique molecular identifier (UMI) can remove bias from amplification noise to a certain extent, clustering for such sparse and high-dimensional large-scale discrete data remains intractable and challenging …”
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Enhancing Neural Network Interpretability with Feature-Aligned Sparse Autoencoders von Marks, Luke, Paren, Alasdair, Krueger, David, Barez, Fazl

“… Sparse Autoencoders (SAEs) have shown promise in improving the interpretability of neural network activations, but can learn features that are not features of the input, limiting their effectiveness. We propose \textsc …”
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