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检索条件"主题词=variational autoencoder"

共 1529 条记录，以下是311-320 订阅

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Helical paths based two-dimensional source discrimination of acoustic emission waveforms from hollow cylindrical structures using variational autoencoder 17

Helical paths based two-dimensional source discrimination of...

引用

Conference on Health Monitoring of Structural and Biological Systems XVII

作者： Lee, Guan-Wei Livadiotis, Stylianos Salamone, Salvatore Univ Texas Austin Dept Civil Architectural & Environm Engn Austin TX 78712 USA

ISBN: (纸本)9781510660830;9781510660847

This work presents acoustic emission (AE) waveform to source coordinates transformation at hollow cylinders facilitated by multiple Lamb mode arrivals due to the cylindrical geometry. variational autoencoder (VAE) is selected to perform waveform source discrimination by capturing the delays in time-of-flights (TOF) between modes described in the transformation. An AE waveform dataset simulated by pencil lead break on a liquid nitrogen tank was collected to validate the proposed approach. The result indicates that VAE is capable of separate AE waveforms by their sources through the targeted delays between mode arrivals.

关键词： Acoustic emission helical paths variational autoencoder

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Spatial domain identification based on variational autoencoder and single sample network 39

Spatial domain identification based on variational autoencod...

引用

39th Youth Academic Annual Conference of Chinese-Association-of-Automation (YAC)

作者： Guo, Wei-Feng Wang, Yuan-Chao Feng, Song-Yan Hu, Xin-Bo Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Peoples R China Zhengzhou Univ State Key Lab Intelligent Agr Power Equipment Zhengzhou 471100 Peoples R China

ISBN: (纸本)9798350390780;9798350379228

Due to its ability to reveal tissue heterogeneity, spatial analytic transcriptomic data has been used to decipher the spatial domain of complex diseases for precise treatment. At present, the problems of high dimensional, sparse and noisy data in the application process are awaiting to be ***, it has not fully leveraged the intercellular molecular interactions, which affect the accuracy of spatial domain identification. In order to solve this problem, the stMVC model is improved by integrating the single sample network method and variational autoencoder, and the spatial domain(SDI-VASSN) is extracted from the transcriptome data of multimodal spatial decomposition. Specifically, the model uses multimodal data of the human dorsolateral prefrontal cortex obtained through 10X Genomics Visium technology. Firstly, from the samples, we used cell specific molecular interaction network (CSN) to calculate the gene interaction network of each cell and extract key genes;Then we encode key genes by one-hot coding;Finally, we use variational autoencoder (VAE) instead of autoencoder (AE) to maximize the probability of the input data to learn the probability distribution of the data, thus input the resulting data into the stMVC model for identifying the spatial domain.

关键词： Spatial resolved transcriptomics Multi-view models Multimodal data Spatial domain identification variational autoencoder

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DEEP variational autoencoder FOR MODELING FUNCTIONAL BRAIN NETWORKS AND ADHD IDENTIFICATION 17

DEEP VARIATIONAL AUTOENCODER FOR MODELING FUNCTIONAL BRAIN N...

引用

IEEE 17th International Symposium on Biomedical Imaging (ISBI)

作者： Qiang, Ning Dong, Qinglin Sun, Yifei Ge, Bao Liu, Tianming Shaanxi Normal Univ Sch Phys & Informat Technol Xian Peoples R China Univ Georgia Dept Comp Sci Athens GA 30602 USA

ISBN: (纸本)9781538693308

In the neuroimaging and brain mapping communities, researchers have proposed a variety of computational methods and tools to learn functional brain networks (FBNs). Recently, it has already been proven that deep learning can be applied on fMRI data with superb representation power over traditional machine learning methods. Limited by the high-dimension of fMRI volumes, deep learning suffers from the lack of data and overfitting. Generative models are known to have intrinsic ability of modeling small dataset and a Deep variational autoencoder (DVAE) is proposed in this work to tackle the challenge of insufficient data and incomplete supervision. The FBNs learned from fMRI were examined to be interpretable and meaningful and it was proven that DVAE has better performance on neuroimaging dataset over traditional models. With an evaluation on ADHD-200 dataset, DVAE performed excellent on classification accuracies on 4 sites.

关键词： Resting-state fMRI variational autoencoder ADHD Deep Learning Generative Learning

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GUIDED variational autoencoder FOR SPEECH ENHANCEMENT WITH A SUPERVISED CLASSIFIER

GUIDED VARIATIONAL AUTOENCODER FOR SPEECH ENHANCEMENT WITH A...

引用

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

作者： Carbajal, Guillaume Richter, Julius Gerkmann, Timo Univ Hamburg Signal Proc SP Hamburg Germany

ISBN: (纸本)9781728176055

Recently, variational autoencoders have been successfully used to learn a probabilistic prior over speech signals, which is then used to perform speech enhancement. However, variational autoencoders are trained on clean speech only, which results in a limited ability of extracting the speech signal from noisy speech compared to supervised approaches. In this paper, we propose to guide the variational autoencoder with a supervised classifier separately trained on noisy speech. The estimated label is a high-level categorical variable describing the speech signal (e.g. speech activity) allowing for a more informed latent distribution compared to the standard variational autoencoder. We evaluate our method with different types of labels on real recordings of different noisy environments. Provided that the label better informs the latent distribution and that the classifier achieves good performance, the proposed approach outperforms the standard variational autoencoder and a conventional neural network-based supervised approach.

关键词： Speech enhancement deep generative model variational autoencoder semi-supervised learning

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Learning robust speech representation with an articulatory-regularized variational autoencoder 22

Learning robust speech representation with an articulatory-r...

引用

Interspeech Conference

作者： Georges, Marc-Antoine Girin, Laurent Schwartz, Jean-Luc Hueber, Thomas Univ Grenoble Alpes CNRS GIPSA Lab F-38000 Grenoble France Univ Grenoble Alpes CNRS LPNC F-38000 Grenoble France

ISBN: (纸本)9781713836902

It is increasingly considered that human speech perception and production both rely on articulatory representations. In this paper, we investigate whether this type of representation could improve the performances of a deep generative model (here a variational autoencoder) trained to encode and decode acoustic speech features. First we develop an articulatory model able to associate articulatory parameters describing the jaw, tongue, lips and velum configurations with vocal tract shapes and spectral features. Then we incorporate these articulatory parameters into a variational autoencoder applied on spectral features by using a regularization technique that constrains part of the latent space to represent articulatory trajectories. We show that this articulatory constraint improves model training by decreasing time to convergence and reconstruction loss at convergence, and yields better performance in a speech denoising task.

关键词： Speech production representation learning variational autoencoder articulatory model speech enhancement

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The Difference Learning of Hidden Layer between autoencoder and variational autoencoder 29

The Difference Learning of Hidden Layer between Autoencoder ...

引用

第29届中国控制与决策会议

作者： Qingyang Xu Zhe Wu Yiqin Yang Li Zhang School of Mechanical Electrical & Information EngineeringShandong University

ISBN: (纸本)9781509046584

autoencoder is an excellent unsupervised learning ***,it can not generate kinds of sample data in the decoding *** autoencoder is a typical generative adversarial net which can generate various data to augment the sample *** this paper,we want to do some research about the information learning in hidden *** the simulation,we compare the hidden layer learning of hidden layer in conventional autoencoder and variational autoencoder.

关键词： autoencoder variational autoencoder Hidden layer learning Probabilistic model MNIST

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A variational autoencoder-Based Secure Transceiver Design Using Deep Learning

A Variational Autoencoder-Based Secure Transceiver Design Us...

引用

IEEE Global Communications Conference (GLOBECOM) on Advanced Technology for 5G Plus

作者： Lin, Chia-Hung Wu, Chao-Chin Chen, Kuan-Fu Lee, Ta-Sung Natl Chiao Tung Univ Inst Commun Engn Hsinchu Taiwan Natl Chiao Tung Univ Ctr mmWave Smart Radar Syst & Technol Hsinchu Taiwan North Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9781728182988

To achieve new applications for 5G communications, physical layer security has recently drawn significant attention. In a wiretap channel system, our goal is to minimize information leakage to an eavesdropper while maximizing the performance of transmission to the desired or legitimate receiver. Complicated systems or channel models make it difficult to design secrecy systems based on the information theory. In this paper, we propose a deep learning-based transceiver design for secrecy systems as an alternative. Specifically, we modify the loss function design of a variational autoencoder, which is a special type of neural network, making it possible to provide both robust data transmission and security in an unsupervised fashion. We further investigate the impact of an imperfect channel state information and use simulation results to prove that our approach can outperform the existing learning-based methods.

关键词： physical layer security wiretap channel deep learning neural networks variational autoencoder

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Transfer Learning Approach for Botnet Detection Based on Recurrent variational autoencoder 3

Transfer Learning Approach for Botnet Detection Based on Rec...

引用

3rd International Workshop on Systems and Network Telemetry and Analytics (SNTA)

作者： Kim, Jeeyung Sim, Alex Kim, Jinoh Wu, Kesheng Hahm, Jaegyoon Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Texas A&M Univ Commerce TX USA KISTI Daejeon South Korea

ISBN: (纸本)9781450379809

Machine Learning (ML) methods have been widely used in Intrusion Detection Systems (IDS). In particular, many botnet detection methods are based on ML. However, due to the fast-evolving nature of network security threats, it is necessary to frequently retrain the ML tools with up-to-date data, especially because data labeling takes a long time and requires a lot of effort, making it difficult to generate training data. We propose transfer learning as a more effective approach for botnet detection, as it can learn from well curated source data and transfer the knowledge to a target problem domain not seen before. We devise an approach that is effective regardless whether or not the data from the target domain is labeled. More specifically, we train a neural network with the Recurrrent Variation autoencoder (RVAE) structure on the source data, and use RVAE to compute anomaly scores for data records from the target domain. In an evaluation of this transfer learning framework, we use CTU-13 dataset as a source domain and a fresh set of network monitoring data as a target domain. Tests show that the proposed transfer learning method is able to detect botnets better than semi-supervised learning method that was trained on the target domain data. The area under Receiver Operating Characteristic is 0.810 for transfer learning, and 0.779 for directly using RVAE on the target domain data.

关键词： botnet detection transfer learning Recurrent Neural Network variational autoencoder

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Behavioral Cloning in Atari Games Using a Combined variational autoencoder and Predictor Model

Behavioral Cloning in Atari Games Using a Combined Variation...

引用

IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Chen, Brian Tandon, Siddhant Gorsich, David Gorodetsky, Alex Veerapaneni, Shravan Univ Michigan Dept Math Ann Arbor MI 48109 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA US Army DEVCOM Ground Vehicle Syst Ctr Warren MI USA

ISBN: (纸本)9781728183923

We explore an approach to behavioral cloning in video games. We are motivated to pursue a learning architecture that is data efficient and provides opportunity for interpreting player strategies and replicating player actions in unseen situations. To this end, we have developed a generative model that learns latent features of a game that can be used for training an action predictor. Specifically, our architecture combines a variational autoencoder with a discriminator mapping the latent space to action predictions (predictor). We compare our model performance to two different behavior cloning architectures: a discriminative model (a Convolutional Neural Network) mapping game states directly to actions, and a variational autoencoder with a predictor trained separately. Finally, we demonstrate how we can use the advantage of generative modeling to sample new states from the latent space of the variational autoencoder to analyze player actions and provide meaning to certain latent features.

关键词： Behavior Cloning variational autoencoder Predictive Models Video Games

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Different Latent Variables Learning in variational autoencoder

Different Latent Variables Learning in Variational Autoencod...

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4th International Conference on Information, Cybernetics and Computational Social Systems (ICCSS)

作者： Xu, Qingyang Yang, Yiqin Wu, Zhe Zhang, Li Shandong Univ Sch Mech Elect & Informat Engn Weihai 264209 Peoples R China

ISBN: (纸本)9781538632574

Unsupervised learning is a good neural network training way. However, the unsupervised learning algorithm is rare. The generative model is an interesting algorithm which can generate the similar data as the sample data by building a probabilistic model of the input data, and it can be used for unsupervised learning. variational autoencoder is a typical generative model which is different from common autoencoder that a probabilistic parameter layer follows the hidden layer. Some new data can be reconstructed according to probabilistic model parameters. The probabilistic model parameter is the latent variable. In this paper, we want to do some research to test the data reconstruct effect of the variational autoencoder by different latent variables. According to the simulation, the more latent variables the more style of the sample is.

关键词： variational autoencoder probabilistic model latent Variable MNIST

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