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检索条件"主题词=Denoising autoencoder"

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Accurately Clustering Single-cell RNA-seq data by Capturing Structural Relations between Cells through Graph Convolutional Network

Accurately Clustering Single-cell RNA-seq data by Capturing ...

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IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Zeng, Yuausong Zhou, Xiang Rao, Jiahua Lu, Yutong Yang, Yuedong Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou 510000 Peoples R China Sun Yat Sen Univ Minist Educ Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9781728162157

Recent advances in single-cell RNA sequencing (scRNA-seq) technologies provide a great opportunity to study gene expression at cellular resolution, and the scRNA-seq data has been routinely conducted to unfold cell heterogeneity and diversity. A critical step for the scRNA-seq analyses is to cluster the same type of cells, and many methods have been developed for cell clustering. However, existing clustering methods are limited to extract the representations from expression data of individual cells, while ignoring the high-order structural relations between cells. Here, we proposed a new method (GraphSCC) to cluster cells based on scRNA-seq data by accounting structural relations between cells through a graph convolutional network. The representation learned from the graph convolutional network, together with another representation output from a denoising autoencoder network, are optimized by a dual self-supervised module for better cell clustering. Extensive experiments indicate that GraphSCC model outperforms state-of-the-art methods in various evaluation metrics on both simulated and real datasets.

关键词： Graph Convolutional Network Single-cell RNA-seq Clustering denoising autoencoder Self-supervised Learning

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Recognition of mild-to-moderate depression based on facial expression and speech 24

Recognition of mild-to-moderate depression based on facial e...

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5th International Conference on Computing, Networks and Internet of Things (CNIOT)

作者： Li, Jinlong Li, Ying Lanzhou Inst Technol Coll Elect Informat Engn Lanzhou Peoples R China

ISBN: (纸本)9798400716751

The behavioral symptoms of patients with mild to moderate depression (MMD) are usually not obvious enough, which poses a challenge to MMD recognition research. A three-level feature construction strategy for facial expression was proposed to fully characterize the differences in facial activity between MMD patients and healthy control groups (HCs). Level 1: Construct geometric features to describe facial activity preliminarily. Level 2: Input geometric features into a denoising autoencoder (DAE) to generate a new hidden layer representation. Level 3: Gaussian mixture model (GMM) further characterizes the hidden layer features. Meanwhile, a speech feature set was constructed based on the fundamental frequency (F0), pitch intensity, mel frequency cepstral coefficients (MFCCs), and syllable pauses of speech. Finally, facial expression and speech were fused at the feature layer, and MMD recognition was carried out based on four classic classification algorithms. The experimental results show that the MMD recognition accuracy of the male and female groups can reach 70.3% and 68.3%, respectively.

关键词： Depression recognition Facial expression Speech denoising autoencoder

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S-VECTOR: A DISCRIMINATIVE REPRESENTATION DERIVED FROM I-VECTOR FOR SPEAKER VERIFICATION 23

S-VECTOR: A DISCRIMINATIVE REPRESENTATION DERIVED FROM I-VEC...

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23rd European Signal Processing Conference (EUSIPCO)

作者： Isik, Yusuf Ziya Erdogan, Hakan Sarikaya, Ruhi TUBITAK BILGEM Gebze Turkey Sabanci Univ Fac Engn & Nat Sci Istanbul Turkey Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9780992862633

Representing data in ways to disentangle and factor out hidden dependencies is a critical step in speaker recognition systems. In this work, we employ deep neural networks (DNN) as a feature extractor to disentangle and emphasize the speaker factors from other sources of variability in the commonly used i-vector features. denoising autoencoder based unsupervised pre-training, random dropout fine-tuning, and Nesterov accelerated gradient based momentum is used in DNN training. Replacing the i-vectors with the resulting speaker vectors (s-vectors), we obtain superior results on NIST SRE corpora on a wide range of operating points using probabilistic linear discriminant analysis (PLDA) back-end.

关键词： speaker verification denoising autoencoder random dropout

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SPEECH FEATURE denoising AND DEREVERBERATION VIA DEEP autoencoderS FOR NOISY REVERBERANT SPEECH RECOGNITION

SPEECH FEATURE DENOISING AND DEREVERBERATION VIA DEEP AUTOEN...

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

作者： Feng, Xue Zhang, Yaodong Glass, James MIT Comp Sci & Artificial Intelligence Lab Cambridge MA 02139 USA

ISBN: (纸本)9781479928934

denoising autoencoders (DAs) have shown success in generating robust features for images, but there has been limited work in applying DAs for speech. In this paper we present a deep denoising autoencoder (DDA) framework that can produce robust speech features for noisy reverberant speech recognition. The DDA is first pre-trained as restricted Boltzmann machines (RBMs) in an unsupervised fashion. Then it is unrolled to autoencoders, and fine-tuned by corresponding clean speech features to learn a nonlinear mapping from noisy to clean features. Acoustic models are re-trained using the reconstructed features from the DDA, and speech recognition is performed. The proposed approach is evaluated on the CHiME-WSJ0 corpus, and shows a 16-25% absolute improvement on the recognition accuracy under various SNRs.

关键词： robust speech recognition feature denoising denoising autoencoder deep neural network

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Robust Multivariate Anomaly-Based Intrusion Detection System for Cyber-Physical Systems 1

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5th International Symposium on Cyber Security Cryptography and Machine Learning (CSCML)

作者： Dutta, Aneet Kumar Negi, Rohit Shukla, Sandeep Kumar Indian Inst Technol Kanpur Dept Comp Sci & Engn C3i Ctr Kanpur Uttar Pradesh India

ISBN: (数字)9783030780869

ISBN: (纸本)9783030780869;9783030780852

Cyber-physical critical infrastructures such as power plants are no longer air-gapped. Due to IP-Convergence, the control systems and sensor/actuator communication networks are often directly or indirectly connected to the Internet. While network intrusion detection can provide certain cyber defense capabilities, that is not sufficient due to covert attacks or insider attacks. Therefore, in recent years, a lot of research is being carried out to detect intrusion by observing anomalies in the plants' physical dynamics. In this work, we propose a robust anomaly detection mechanism based on a semi-supervised machine learning technique allowing us near real-time localization of attacks. Deep neural network architecture is used to detect anomaly - based on reconstruction error. We demonstrate our method's efficacy on the SWaT dataset. Our method outperforms other existing anomaly detection techniques with an AUC score of 0.9275.

关键词： SWaT dataset Neural networks Robustness Localization autoencoder denoising autoencoder Vulnerabilty Cyber-physical system Industrial control system SCADA MODBUS Intrusion detection system

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Loop Closure Detection for Visual SLAM Systems Using Deep Neural Networks 34

Loop Closure Detection for Visual SLAM Systems Using Deep Ne...

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34th Chinese Control Conference (CCC)

作者： Gao, Xiang Zhang, Tao Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9789881563897

The detection of loop closure is of essential importance in visual simultaneous localization and mapping systems. It can reduce the accumulating drift of localization algorithms if the loops are checked correctly. Traditional loop closure detection approaches take advantage of Bag-of-Words model, which clusters the feature descriptors as words and measures the similarity between the observations in the word space. However, the features are usually designed artificially and may not be suitable for data from new-coming sensors. In this paper a novel loop closure detection approach is proposed that learns features from raw data using deep neural networks instead of common visual features. We discuss the details of the method of training neural networks. Experiments on an open dataset are also demonstrated to evaluate the performance of the proposed method. It can be seen that the neural network is feasible to solve this problem.

关键词： Simultaneous Localization and Mapping Loop Closure Detection Deep Neural Networks denoising autoencoder

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Convolution by Evolution Differentiable Pattern Producing Networks 16

Convolution by Evolution Differentiable Pattern Producing Ne...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Fernando, Chrisantha Banarse, Dylan Reynolds, Malcolm Besse, Frederic Pfau, David Jaderberg, Max Lanctot, Marc Wierstra, Daan Google DeepMind London England

ISBN: (纸本)9781450342063

In this work we introduce a differentiable version of the Compositional Pattern Producing Network, called the DPPN. Unlike a standard CPPN, the topology of a DPPN is evolved but the weights are learned. A Lamarckian algorithm, that combines evolution and learning, produces DPPNs to reconstruct an image. Our main result is that DPPNs can be evolved/trained to compress the weights of a denoising autoencoder from 157684 to roughly 200 parameters, while achieving a reconstruction accuracy comparable to a fully connected network with more than two orders of magnitude more parameters. The regularization ability of the DPPN allows it to rediscover (approximate) convolutional network architectures embedded within a fully connected architecture. Such convolutional architectures are the current state of the art for many computer vision applications, so it is satisfying that DPPNs are capable of discovering this structure rather than having to build it in by design. DPPNs exhibit better generalization when tested on the Omniglot dataset after being trained on MNIST, than directly encoded fully connected autoencoders. DPPNs are therefore a new framework for integrating learning and evolution.

关键词： CPPNs Compositional Pattern Producing Networks denoising autoencoder MNIST

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Unsupervised Detection of Anomalous Behavior in Wireless Devices based on Auto-Encoders

Unsupervised Detection of Anomalous Behavior in Wireless Dev...

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IEEE/IFIP Network Operations and Management Symposium (NOMS)

作者： Albasir, A. Hu, Q. Al-tekreeti, M. Naik, K. Naik, N. Kozlowski, A. J. Goel, N. Univ Waterloo Waterloo ON Canada Minist Def Def Sch CIS London England Cistech Ltd Ottawa ON Canada

ISBN: (纸本)9781728149738

A major problem of wireless devices is the detection of security threats in an efficient manner. Several recent incidents show that malicious applications (apps) can find their ways to online markets (e.g., Google Play Store) and be available for download and installation. Such malicious apps can collect sensitive data from millions of users and send them to a third-party servers. In this paper, we propose a methodology that leverages the power consumption of wireless devices to build a model that makes them more robust to the presence of malicious apps. The method consists of two stages: (i) Feature Extraction where stacked Restricted Boltzmann Machine (RBM) autoencoders (AE) and Principal Component Analysis (PCA) are used to extract features vector based on AE's reconstruction errors. (ii) Classifier where One-Class Support Vector Machine is trained to perform the classification task. The validation of the methodology is performed on a real measurements dataset. The obtained results show a good potential and prove that AEs' reconstruction error can be used as a good discriminating feature. The obtained detection accuracy surpasses previously reported techniques, where it reaches up to similar to 98% in some scenarios.

关键词： Malware Detection Power Consumption Information denoising autoencoder Wireless Devices

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ROBUST BELIEF STATE SPACE REPRESENTATION FOR STATISTICAL DIALOGUE MANAGERS USING DEEP autoencoderS

ROBUST BELIEF STATE SPACE REPRESENTATION FOR STATISTICAL DIA...

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IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)

作者： Lygerakis, Fotios Diakoloulas, Vassilios Lagoudakis, Michail Kotti, Margarita Tech Univ Crete Sch Elect & Comp Engn Iraklion Greece Toshiba Res Cambridge Speech Technol Grp Cambridge England

ISBN: (纸本)9781728103068

Statistical Dialogue Systems (SDS) have proved their humongous potential over the past few years. However, the lack of efficient and robust representations of the belief state (BS) space refrains them from revealing their full potential. There is a great need for automatic BS representations, which will replace the old hand-crafted, variable-length ones. To tackle those problems, we introduce a novel use of autoencoders (AEs). Our goal is to obtain a low-dimensional, fixed-length, and compact, yet robust representation of the BS space. We investigate the use of dense AE, denoising AE (DAE) and Variational denoising AE (VDAE), which we combine with GP-SARSA to learn dialogue policies in the PyDial toolkit. In this framework, the BS is normally represented in a relatively compact, but still redundant summary space which is obtained through a heuristic mapping of the original master space. We show that all the proposed AE-based representations consistently outperform the summary BS representation. Especially, as the Semantic Error Rate (SER) increases, the DAE/VDAE-based representations obtain state-of-the-art and sample efficient performance.

关键词： denoising autoencoder variational autoencoder statistical dialogue system dialogue manager belief state representation

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Front-end Feature Compensation and denoising for Noise Robust Speech Emotion Recognition 20

Front-end Feature Compensation and Denoising for Noise Robus...

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Interspeech Conference

作者： Chakraborty, Rupayan Panda, Ashish Pandharipande, Meghna Joshi, Sonal Kopparapu, Sunil Kumar TCS Res & Innovat Mumbai Maharashtra India

Front-end processing is one of the ways to impart noise robustness to speech emotion recognition systems in mismatched scenarios. Here, we implement and compare different front-end robustness techniques for their efficacy in speech emotion recognition. First, we use a feature compensation technique based on the Vector Taylor Series (VTS) expansion of noisy Mel-Frequency Cepstral Coefficents (MFCCs). Next, we improve upon the feature compensation technique by using the VTS expansion with auditory masking formulation. We have also looked into the applicability of 10(th) -root compression in MFCC computation. Further, a Time Delay Neural Network based denoising autoencoder (TDNN-DAE) is implemented to estimate the clean MFCCs from the noisy MFCCs. These techniques have not been investigated yet for their suitability to robust speech emotion recognition task. The performance of these front-end techniques are compared with the Non-Negative Matrix Factorization (NMF) based front-end. Relying on extensive experiments done on two standard databases (EmoDB and IEMOCAP), contaminated with 5 types of noise, we show that these techniques provide significant performance gain in emotion recognition task. We also show that along with front-end compensation, applying feature selection to non-MFCC high-level descriptors results in better performance.

关键词： Speech emotion recognition noise robustness denoising autoencoder feature compensation auditory masking

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