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IEEE 6th International Congress on Big Data (BigData Congress)

作者： Chen, Huaming Shen, Jun Wang, Lei Song, Jiangning Univ Wollongong Sch Comp & Informat Technol Wollongong NSW Australia Monash Univ Dept Biochem & Mol Biol Melbourne Vic Australia

ISBN: (纸本)9781538619964

In big data research related to bioinformatics, one of the most critical areas is proteomics. In this paper, we focus on the protein-protein interactions, especially on pathogen-host protein-protein interactions (PHPPIs), which reveals the critical molecular process in biology. Conventionally, biologists apply in-lab methods, including small-scale biochemical, biophysical, genetic experiments and large-scale experiment methods (e.g. yeast-two-hybrid analysis), to identify the interactions. These in-lab methods are time consuming and labor intensive. Since the interactions between proteins from different species play very critical roles for both the infectious diseases and drug design, the motivation behind this study is to provide a basic framework for biologists, which is based on big data analytics and deep learning models. Our work contributes in leveraging unsupervised learning model, in which we focus on stacked denoising autoencoders, to achieve a more efficient prediction performance on PHPPI. In this paper, we further detail the framework based on unsupervised learning model for PHPPI researches, while curating a large imbalanced PHPPI dataset. Our model demonstrates a better result with the unsupervised learning model on PHPPI dataset.

关键词： big data PHPPI denoising autoencoder prediction machine learning

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Learning via denoising autoencoder on 5G NR Phase Noise Estimation

Learning via Denoising Autoencoder on 5G NR Phase Noise Esti...

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IEEE Global Communications Conference (GLOBECOM)

作者： Lin, Mu-sheng Kwon, Hyukjoon Samsung Semicond Inc San Diego CA 92121 USA

ISBN: (纸本)9781728181042

In this paper, on phase noise of 5G NR mmWave systems, we propose a learning-based common phase error (CPE) estimation algorithm based on the denoising autoencoder serving as a nonlinear filter on the existing CPE estimators. The proposed algorithm learns the low dimension manifold of phase noise distributions with high probability. Traditional CPE methods have limitation when the time domain pilots are few, which causes degraded system performance with CPE interpolation. Besides accurate CPE estimation, the proposed method is more robust to various FR2 channels, Doppler effects, and the numerologies. Simulation results show that block error rate (BLER) could be improved up to 1.43 dB on SNR.

关键词： Common Phase Error learning denoising autoencoder neural network 5G NR FR2

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MODELING GENDER INFORMATION FOR EMOTION RECOGNITION USING denoising autoencoder

MODELING GENDER INFORMATION FOR EMOTION RECOGNITION USING DE...

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IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Xia, Rui Deng, Jun Schuller, Bjoern Liu, Yang Univ Texas Dallas Dept Comp Sci Dallas TX 75230 USA Tech Univ Munich MMK Machine Intelligence & Signal Proc Grp Munich Germany Imperial Coll London Dept Comp London England

ISBN: (纸本)9781479928934

The denoising autoencoder (DAE) has been successfully applied to acoustic emotion recognition lately. In this paper, we adopt the framework of the modified DAE introduced in [1] that projects the input signal to two different hidden representations, for neutral and emotional speech respectively, and uses the emotional representation for the classification task. We propose to model gender information for more robust emotional representation in this work. For neutral representation, male and female dependent DAEs are built using non-emotional speech with the aim of capturing distinct information between the two genders. The emotional hidden representation is shared for the two genders in order to model more emotion specific characteristics, and is used as features in a back-end classifier for emotion recognition. We propose different optimization objectives in training the DAEs. Our experimental results show improvement on unweighted accuracy compared with previous work using the modified DAE method and the classifiers using the standard static features. Further performance gain can be achieved by structural level system combination.

关键词： Emotion recognition denoising autoencoder Gender

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Knowledge-Experience Graph with denoising autoencoder for Zero-Shot Learning in Visual Cognitive Development 27th

Knowledge-Experience Graph with Denoising Autoencoder for Ze...

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27th International Conference on Neural Information Processing

作者： Zhang, Xinyue Yang, Xu Liu, Zhiyong Zhang, Lu Ren, Dongchun Fan, Mingyu Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Meituan Dianping Grp Beijing 100190 Peoples R China

ISBN: (纸本)9783030638221;9783030638238

Visual cognitive development is vital for intelligent robots to handle various types of visual tasks rather than predefined ones. It can transfer the classification ability from an original model to a novel task. However, the high reliance on large amounts of data hinders its development. The energy it costs to adjust to the novel tasks is also a tough problem. Thus we propose a model called knowledge-experience graph (KEG) to imitate the mechanisms of human brains. With the help of social knowledge stored in the knowledge graph, the novel classes can be easily added. The combination of the experience via denoising autoencoder (DAE) also takes the relationship in the visual space into account. With the propagation of information among the graph by graph convolutional network (GCN), KEG generates the classifier of the novel tasks effectively. Experiments show that KEG improves the classification accuracy of novel categories on zero-shot learning and accomplishes visual cognitive development to a certain extent.

关键词： GCN Zero-shot learning Cross-task learning Cognitive development Image classification denoising autoencoder

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Reverberant Speech Recognition Based on denoising autoencoder

Reverberant Speech Recognition Based on Denoising Autoencode...

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14th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2013)

作者： Ishii, Takaaki Komiyama, Hiroki Shinozaki, Takahiro Horiuchi, Yasuo Kuroiwa, Shingo Chiba Univ Grad Sch Adv Integrat Sci Div Informat Sci Chiba Japan Tokyo Inst Technol Interdisciplinary Grad Sch Sci & Engn Dept Informat Proc Tokyo Japan

ISBN: (纸本)9781629934433

denoising autoencoder is applied to reverberant speech recognition as a noise robust front-end to reconstruct clean speech spectrum from noisy input. In order to capture context effects of speech sounds, a window of multiple short-windowed spectral frames are concatenated to form a single input vector. Additionally, a combination of short and long-term spectra is investigated to properly handle long impulse response of reverberation while keeping necessary time resolution for speech recognition. Experiments are performed using the CENSREC-4 dataset that is designed as an evaluation framework for distant-talking speech recognition. Experimental results show that the proposed denoising autoencoder based front-end using the short-windowed spectra gives better results than conventional methods. By combining the long-term spectra, further improvement is obtained. The recognition accuracy by the proposed method using the short and long-term spectra is 97.0% for the open condition test set of the dataset, whereas it is 87.8% when a multi condition training based baseline is used. As a supplemental experiment, large vocabulary speech recognition is also performed and the effectiveness of the proposed method has been confirmed.

关键词： denoising autoencoder reverberant speech recognition restricted Boltzmann machine distant-talking speech recognition CENSREC-4

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Anomaly Root Cause Analysis forWind Turbines Based on denoising autoencoder and Sparse Estimation 12

Anomaly Root Cause Analysis forWind Turbines Based on Denois...

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IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)

作者： Du, Songtao Wan, Yiming Zhang, Cong Zhang, Sihang Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Wuhan 430074 Peoples R China Beijing Inst Control Engn Beijing 100094 Peoples R China

ISBN: (纸本)9798350321050

Wind turbine condition monitoring has been extensively studied to reduce maintenance costs. Although there exist a vast amount of literature on anomaly detection for wind turbine, anomaly root cause analysis has not been fully addressed so far. To cope with this problem, we propose a denoising autoencoder (DAE) based anomaly detector and performs anomaly root cause analysis using sparse estimation. For anomaly detection, deep denoising autoencoder is learned with normal history data, with enhanced robustness compared to the conventional autoencoder. The reconstruction error from the DAE is further evaluated by the exponentially weighted moving average control chart (EWMA) to reduce the false positive rate. After anomaly detection, root cause analysis performs sparse fault estimation, with the assumption that a small number of observed variables are affected under the abnormal condition. The fault estimates are then leveraged to identify the variables most relevant to the underlying anomaly root causes. Real cases on a public dataset demonstrate the effectiveness of the proposed method.

关键词： anomaly detection root cause analysis denoising autoencoder sparse fault estimation wind turbine

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Revolutionizing Cancer Diagnosis Through Hybrid Self-supervised Deep Learning: EfficientNet with denoising autoencoder for Semantic Segmentation of Histopathological Images 27th

Revolutionizing Cancer Diagnosis Through Hybrid Self-supervi...

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27th Annual Conference on Medical Image Understanding and Analysis (MIUA)

作者： Hammouda, Mostafa A. Khaled, Marwan Ali, Hesham Selim, Sahar Elattar, Mustafa Nile Univ Sch Informat Technol & Comp Sci Giza 12677 Egypt Nile Univ Med Imaging & Image Proc Res Grp Ctr Informat Sci Giza 12677 Egypt

ISBN: (纸本)9783031485923;9783031485930

Machine Learning technologies are being developed day after day, especially in the medical field. New approaches, algorithms and architectures are implemented to increase the efficiency and accuracy of diagnosis and segmentation. Deep learning approaches have proven their efficiency;these approaches include architectures like EfficientNet and denoising autoencoder. Accurate segmentation of nuclei in histopathological images is essential for the diagnosis and prognosis of diseases like cancer. In this paper, we propose a novel method for semantic segmentation of nuclei using EfficientNet and denoising Auto-encoder on the PanNuke dataset. The denoising auto-encoder pre-processing step is used to enhance the feature representations of input images, and EfficientNet is the model that has been used as the semantic segmentation model. Our proposed method achieved state-of-the-art results, outperforming most of the previously proposed methods by a significant margin, as our proposed method achieved a higher Dice score of 83.33 compared to the previous related work methods, which achieved scores varying from 69.3 to 80.28. The efficiency of our proposed approach will be demonstrated in this paper by discussing, exploring, and comparing it with previously proposed methods and their results.

关键词： semantic segmentation histopathological images self-supervised deep learning EfficientNet denoising autoencoder

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Improved Automatic Speech Recognition using Subband Temporal Envelope Features and Time-delay Neural Network denoising autoencoder 18

Improved Automatic Speech Recognition using Subband Temporal...

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18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017)

作者： Cong-Thanh Do Stylianou, Yannis Toshiba Res Europe 208 Cambridge Sci Pk Cambridge England

ISBN: (纸本)9781510848764

This paper investigates the use of perceptually-motivated sub band temporal envelope (STE) features and time-delay neural network (TDNN) denoising autoencoder (DAE) to improve deep neural network (DNN)-based automatic speech recognition (ASR). STEs are estimated by full-wave rectification and low-pass filtering of band-passed speech using a Gammatone filter-bank. TDNNs are used either as DAE or acoustic models. ASR experiments are performed on Aurora-4 corpus. STE features provide 2.2% and 3.7% relative word error rate (WER) reduction compared to conventional log-mel filter-bank (FBANK) features when used in ASR systems using DNN and TDNN as acoustic models, respectively. Features enhanced by TDNN DAE are better recognized with ASR system using DNN acoustic models than using TDNN acoustic models. Improved ASR performance is obtained when features enhanced by TDNN DAE are used in ASR system using DNN acoustic models. In this scenario, using STE features provides 9.8% relative WER reduction compared to when using FBANK features.

关键词： Subband temporal envelope denoising autoencoder time-delay neural network deep neural network acoustic features

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TWO-STAGE NOISE AWARE TRAINING USING ASYMMETRIC DEEP denoising autoencoder 41

TWO-STAGE NOISE AWARE TRAINING USING ASYMMETRIC DEEP DENOISI...

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41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Lee, Kang Hyun Kang, Shin Jae Kang, Woo Hyun Kim, Nam Soo Seoul Natl Univ Dept Elect & Comp Engn Gwanak POB 34 Seoul 151744 South Korea Seoul Natl Univ INMC Gwanak POB 34 Seoul 151744 South Korea

ISBN: (纸本)9781479999880

Ever since the deep neural network (DNN)-based acoustic model appeared, the recognition performance of automatic speech recognition has been greatly improved. Due to this achievement, various researches on DNN-based technique for noise robustness are also in progress. Among these approaches, the noise-aware training (NAT) technique which aims to improve the inherent robustness of DNN using noise estimates has shown remarkable performance. However, despite the great performance, we cannot be certain whether NAT is an optimal method for sufficiently utilizing the inherent robustness of DNN. In this paper, we propose a novel technique which helps the DNN to address the complex connection between the input and target vectors of NAT smoothly. The proposed method outperformed the conventional NAT in Aurora-5 task.

关键词： Deep neural networks (DNNs) robust speech recognition noise aware training (NAT) denoising autoencoder

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denoising autoencoder based Long non-coding RNA-Disease Association Prediction

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Procedia Computer Science 2023年 218卷 836-844页

作者： C.P. Gopikrishnan Manu Madhavan

Long non-coding RNAs (lncRNAs) are recent listing in RNA Bioinformatics, which is getting more popular due to their important functional roles. According to the available research, lncRNAs play an essential role in multiple complex diseases. Determining the function of lncRNAs in diseases will help to comprehend many missing links in the disease mechanism. Predicting lncRNAdisease association (LDA) is a crucial stage in this process which is getting at most research interest nowadays. The developments in machine learning and deep learning technologies influenced recent research on LDA models. Most of the methods analyse the interactions of lncRNA with other molecules such as microRNA (miRNA), messenger RNA(mRNA), and proteins. Deep learning models, specifically from autoencoder classes, used extensively in unsupervised learning of features from these associations. This research paper proposes a denoising autoencoder (DAE) based LDA prediction approach. The proposed model uses DAE to learn lncRNA-disease representations from multiple biological networks such as lncRNA-miRNA, miRNA-disease, and disease-lncRNA interactions. The experiments show that the model outperforms other state-of-the-art LDA models concerning the area under the ROC curve (AUC-ROC, 0.94) and the area under precision-recall (AUPR, 0.9592).

关键词： Long non-coding RNA autoencoder denoising autoencoder Representation learning Deep learning Function similarity network

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