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A synchronized heterogeneous autoencoder with feature-level and label-level knowledge distillation for the recommendation

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 106卷 104494-104494页

作者： Pan, Yiteng He, Fazhi Yan, Xiaohu Li, Haoran DongGuan Univ Technol Sch Cyberspace Secur Dongguan Peoples R China Wuhan Univ Sch Comp Sci Wuhan Peoples R China Shenzhen Polytech Sch Artificial Intelligence Shenzhen Peoples R China Xidian Univ ISN State Key Lab Xian Peoples R China

Motivated by the success of deep learning techniques, there are numerous deep learning models developed for recommender systems. User-oriented autoencoder (UAE) and item-oriented autoencoder (IAE) are two typical approaches developed for the recommendation by learning features from items or users respectively. Existing works demonstrate that IAE model outperforms UAE model for rating prediction tasks, while UAE model outperforms IAE model for top-N recommendation task. This fact motivates us to develop a new synchronized heterogeneous autoencoder (SHAE) for top-N recommendation by considering both features learned by IAE and UAE models. Especially, we develop two novel heterogeneous knowledge distillation methods in feature level and label-level to build relations between IAE and UAE models. Compared with matrix factorization approaches, our methods are more efficient with acceptable computational complexity for the recommendation. We conduct comprehensive experiments on public datasets to compare with several state-of-the-art approaches. Experimental results demonstrate that the proposed method significantly outperforms other comparisons for top-N recommendation tasks.

关键词： Deep learning Recommender systems denoising autoencoder Knowledge distillation

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denoising transthoracic echocardiographic images in regional wall motion abnormality using deep learning techniques

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SOFT COMPUTING 2023年

作者： Beevi, A. Shamla Ratheesha, S. Kalady, Saidalavi Chackola, Jenu James Natl Inst Technol Calicut Kozhikode Kerala India SOTI India Pvt Ltd Kochi Kerala India Aster MIMS Hosp Kottakkal Malappuram Kerala India

Image analysis and classification perform well in pre-processed noise-free images than in corrupted images. Synthetic aperture radar (SAR) images, Ultrasound (US) medical images, etc. exhibit speckle noise, which has a multiplicative and granular behavior. In the existing techniques, the autoencoders are used to implement a deep learning-based denoising method specifically for US images. The traditional image denoising techniques as well as deep learning techniques for image denoising. In this paper, we have proposed a deep learning-based model called, Convolutional-based improved despeckling autoencoder (CIDAE) for denoising transthoracic echocardiographic images. The dataset for the network has been collected from patients having Regional Wall Motion Abnormality (RWMA). There were 294 subjects with routine transthoracic examinations, consisting of 151 RWMA and 143 normal hearts (55.7 percent female, ages 20-75 years). The potential of the proposed DL algorithms was evaluated visually and quantitatively using the Structural Similarity Index Measure (SSIM), Peak Signal Noise Ratio (PSNR), and Mean Squared Error (MSE). Our results demonstrate the significance of the proposed CIDAE for denoising echo images of patients with RWMA and structurally normal hearts with a promising p-value < 0.0001.

关键词： Deep learning denoising autoencoder Speckle noise Ultrasound images

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A Deep Non-negative Matrix Factorization Model for Big Data Representation Learning

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FRONTIERS IN NEUROROBOTICS 2021年 15卷 701194页

作者： Chen, Zhikui Jin, Shan Liu, Runze Zhang, Jianing Dalian Univ Technol Sch Software Dalian Peoples R China

Nowadays, deep representations have been attracting much attention owing to the great performance in various tasks. However, the interpretability of deep representations poses a vast challenge on real-world applications. To alleviate the challenge, a deep matrix factorization method with non-negative constraints is proposed to learn deep part-based representations of interpretability for big data in this paper. Specifically, a deep architecture with a supervisor network suppressing noise in data and a student network learning deep representations of interpretability is designed, which is an end-to-end framework for pattern mining. Furthermore, to train the deep matrix factorization architecture, an interpretability loss is defined, including a symmetric loss, an apposition loss, and a non-negative constraint loss, which can ensure the knowledge transfer from the supervisor network to the student network, enhancing the robustness of deep representations. Finally, extensive experimental results on two benchmark datasets demonstrate the superiority of the deep matrix factorization method.

关键词： non-negative matrix factorization deep representation learning denoising autoencoder interpretability supervisor network

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ADAGE signature analysis: differential expression analysis with data-defined gene sets

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BMC BIOINFORMATICS 2017年第1期18卷 512-512页

作者： Tan, Jie Huyck, Matthew Hu, Dongbo Zelaya, Rene A. Hogan, Deborah A. Greene, Casey S. Geisel Sch Med Dartmouth Dept Mol & Syst Biol Hanover NH 03755 USA Univ Penn Dept Syst Pharmacol & Translat Therapeut Philadelphia PA 19104 USA Geisel Sch Med Dartmouth Dept Microbiol & Immunol Hanover NH 03755 USA

Background: Gene set enrichment analysis and overrepresentation analyses are commonly used methods to determine the biological processes affected by a differential expression experiment. This approach requires biologically relevant gene sets, which are currently curated manually, limiting their availability and accuracy in many organisms without extensively curated resources. New feature learning approaches can now be paired with existing data collections to directly extract functional gene sets from big data. Results: Here we introduce a method to identify perturbed processes. In contrast with methods that use curated gene sets, this approach uses signatures extracted from public expression data. We first extract expression signatures from public data using ADAGE, a neural network-based feature extraction approach. We next identify signatures that are differentially active under a given treatment. Our results demonstrate that these signatures represent biological processes that are perturbed by the experiment. Because these signatures are directly learned from data without supervision, they can identify uncurated or novel biological processes. We implemented ADAGE signature analysis for the bacterial pathogen Pseudomonas aeruginosa. For the convenience of different user groups, we implemented both an R package (ADAGEpath) and a web server (http://***) to run these analyses. Both are open-source to allow easy expansion to other organisms or signature generation methods. We applied ADAGE signature analysis to an example dataset in which wild-type and Delta anr mutant cells were grown as biofilms on the Cystic Fibrosis genotype bronchial epithelial cells. We mapped active signatures in the dataset to KEGG pathways and compared with pathways identified using GSEA. The two approaches generally return consistent results;however, ADAGE signature analysis also identified a signature that revealed the molecularly supported link between the MexT regulon and A

关键词： Gene set analysis denoising autoencoder Unsupervised feature extraction Data-defined gene set Pseudomonas Aeruginosa

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Lifted Bregman training of neural networks

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2023年第1期24卷 10909-10959页

作者： Xiaoyu Wang Martin Benning Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge UK School of Mathematical Sciences Queen Mary University of London London UK

We introduce a novel mathematical formulation for the training of feed-forward neural networks with (potentially non-smooth) proximal maps as activation functions. This formulation is based on Bregman distances and a key advantage is that its partial derivatives with respect to the network's parameters do not require the computation of derivatives of the network's activation functions. Instead of estimating the parameters with a combination of first-order optimisation method and back-propagation (as is the state-of-the-art), we propose the use of non-smooth first-order optimisation methods that exploit the specific structure of the novel formulation. We present several numerical results that demonstrate that these training approaches can be equally well or even better suited for the training of neural network-based classifiers and (denoising) autoencoders with sparse coding compared to more conventional training frameworks.

关键词： lifted network training distributed optimisation Bregman distances sparse autoencoder denoising autoencoder classification compressed sensing

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A GAN noise modeling based blind denoising method for guided waves

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MEASUREMENT 2022年 188卷 110596-110596页

作者： Cui, Xiushi Li, Dongsheng Li, Ziqi Ou, Jinping Dalian Univ Technol Sch Civil Engn Dalian 116024 Liaoning Peoples R China

In the detection using guided waves, the signal often carries a high level of non-Gaussian noise. The traditional denoising method cannot estimate and use the prior information of the signal, which leads to poor denoising effect. To tackle this problem, this paper proposed a denoising network based on the combination of generative adversarial network (GAN) and autoencoder (AE). First, GAN is used to estimate the distribution characteristics of the extracted noise and generate samples. Second, according to the characteristics of the guided wave, a pair of datasets are generated to train DAE network. The trained denoising AE has strong robustness. As a result, the proposed GAN-AE based denoiser (GAD) can effectively can effectively reduce the noise level and has the ability to accurately recover the peak time of the wave packet. In particular, the proposed method significantly outperforms conventional denoising methods in low signal-to-noise (SNR) conditions.

关键词： Guided waves Generative adversarial network denoising autoencoder Prior information non-Gaussian features

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Minimizing Illumination Effect in License Plate Recognition

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TEHNICKI VJESNIK-TECHNICAL GAZETTE 2021年第2期28卷 363-369页

作者： Kim, Jae-Seoung Whangbo, Taeg-Keun Gachon Univ Dept Comp Engn Coll IT Seongnam Si South Korea

The intelligent transportation system is a key technology for efficient traffic control that has been applied in various fields. The existing intelligent transportation system detects the license plates of vehicles mainly through image feature analysis technology by using image-processing techniques. While this method has the advantage of quickly recognizing license plates by simple computing when the environment for recognizing license plate images is favourable, its accuracy is significantly compromised by various environmental changes. This study proposes a method using Faster region-based CNN (R-CNN) and denoising autoencoder technology to improve the recognition performance for tilted and broken plates and false recognition caused by illumination effects in the access control automation system installed at construction sites where these poor conditions frequently occur. This study investigated 3,000 images collected from actual construction sites, comparing the proposed method with the existing Faster R-CNN for license plates affected by various illumination environments, and found an accuracy improvement of more than 30%.

关键词： denoising autoencoder Faster R-CNN License Plate Recognition

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Latent-space embedding of expression data identifies gene signatures from sputum samples of asthmatic patients

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BMC BIOINFORMATICS 2020年第1期21卷 457-457页

作者： Lou, Shaoke Li, Tianxiao Spakowicz, Daniel Yan, Xiting Chupp, Geoffrey Lowell Gerstein, Mark Yale Univ Program Computat Biol & Bioinformat New Haven CT 06520 USA Yale Univ Dept Mol Biophys & Biochem New Haven CT 06520 USA Ohio State Univ Div Med Oncol Columbus OH 43210 USA Yale Sch Med Pulm & Crit Care New Haven CT 06520 USA

BackgroundThe pathogenesis of asthma is a complex process involving multiple genes and pathways. Identifying biomarkers from asthma datasets, especially those that include heterogeneous subpopulations, is challenging. Potentially, autoencoders provide ideal frameworks for such tasks as they can embed complex, noisy high-dimensional gene expression data into a low-dimensional latent space in an unsupervised fashion, enabling us to extract distinguishing features from expression ***, we developed a framework combining a denoising autoencoder and a supervised learning classifier to identify gene signatures related to asthma severity. Using the trained autoencoder with 50 hidden units, we found that hierarchical clustering on the low-dimensional embedding corresponds well with previously defined and clinically relevant clusters of patients. Moreover, each hidden unit has contributions from each of the genes, and pathway analysis of these contributions shows that the hidden units are significantly enriched in known asthma-related pathways. We then used genes that contribute most to the hidden units to develop a secondary random-forest classifier for directly predicting asthma severity. The feature importance metric from this classifier identified a signature based on 50 key genes, which are associated with severity. Furthermore, we can use these key genes to successfully estimate FEV1/FVC ratios across patients, via support-vector-machine *** found that the denoising autoencoder framework can extract meaningful patterns corresponding to functional gene groups and patient clusters from the gene expression of asthma patients.

关键词： Asthma Asthma subtypes denoising autoencoder Biomarker Non-invasive

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Electricity theft detection using big data and genetic algorithm in electric power systems

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ELECTRIC POWER SYSTEMS RESEARCH 2022年第0期209卷 1页

作者： Shehzad, Faisal Javaid, Nadeem Aslam, Sheraz Javed, Muhammad Umar COMSATS Univ Islamabad Dept Comp Sci Islamabad 44000 Pakistan Cyprus Univ Technol Dept Elect Engn Comp Engn & Informat CY-3036 Limassol Cyprus

Non-technical losses (NTLs) are one of the major causes of revenue losses for electric utilities. In the literature, various machine learning (ML)/deep learning (DL) approaches are employed to detect NTLs. The existing studies are mostly concerned with tuning the hyperparameters of ML/DL methods for efficient detection of NTL, i.e., electricity theft detection. Some of them focus on the selection of prominent features from data to improve the performance of electricity theft detection. However, the curse of dimensionality affects the generalization ability of ML/DL classifiers and leads to computational, storage, and overfitting problems. Therefore, to deal with the above-mentioned issues, this study proposes a system based on metaheuristic techniques (artificial bee colony and genetic algorithm) and denoising autoencoder for electricity theft detection using big data in electric power systems. The former (metaheuristics) are used to select prominent features, while the latter is utilized to extract high variance features from electricity consumption data. Firstly, 11 new features are synthesized using statistical and electrical parameters from the user's consumption history. Then, the synthesized features are used as input to metaheuristic techniques to find a subset of optimal features. Finally, the optimal features are fed as input to the denoising autoencoder to extract features with high variance. The ability of both metaheuristic and autoencoder techniques to select and extract features is measured using a support vector machine. The proposed system reduces the overfitting, storage, and computational overhead of ML classifiers. Moreover, we perform several experiments to verify the effectiveness of our proposed system and results reveal that the proposed system has better performance than its counterparts.

关键词： Electricity theft detection Power systems Machine learning denoising autoencoder Metaheuristics PRECON SGCC

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Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction

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ENERGIES 2021年第8期14卷 2163-2163页

作者： Berghout, Tarek Benbouzid, Mohamed Mouss, Leila-Hayet Univ Batna 2 Lab Automat & Mfg Engn Batna 05000 Algeria Univ Brest Inst RechercheDupuy Lome UMR CNRS 6027 F-29238 Brest France Shanghai Maritime Univ Logist Engn Coll Shanghai 201306 Peoples R China

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios, data-driven research has been directed towards recovering them by imposing accelerated life tests. Consequently, insufficiently recovered features due to rapid damage propagation seem more likely to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to create inconsistency related problems between samples, and teacher-given label behaviors led to more ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks, a new autoencoder has been designed as an additional source that could correlate inputs and labels by exploiting label information in a completely unsupervised learning scheme. Additionally, its stacked denoising version seems to more robustly be able to recover them for new unseen data. Due to the non-stationary and sequentially driven nature of samples, recovered representations have been fed into a transfer learning, convolutional, long-short-term memory neural network for further meaningful learning representations. The assessment procedures were benchmarked against recent methods under different training datasets. The obtained results led to more efficiency confirming the strength of the new learning path.

关键词： bearings prognosis remaining useful life data-driven knowledge-driven transfer learning labels information exploiting labels denoising autoencoder convolutional LSTM

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