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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是311-320 订阅

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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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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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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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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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Generative Active Learning with variational autoencoder for Radiology Data Generation in Veterinary Medicine 2

Generative Active Learning with Variational Autoencoder for ...

引用

2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Lee, In-Gyu Oh, Jun-Young Yu, Hee-Jung Kim, Jae-Hwan Eom, Ki-Dong Jeong, Ji-Hoon Chungbuk Natl Univ Dept Comp Sci Cheongju South Korea Konkuk Univ Dept Vet Med Imaging Seoul South Korea

ISBN: (纸本)9798350354102;9798350354096

Recently, with increasing interest in pet healthcare, the demand for computer-aided diagnosis (CAD) systems in veterinary medicine has increased. The development of veterinary CAD has stagnated due to a lack of sufficient radiology data. To overcome the challenge, we propose a generative active learning framework based on a variational autoencoder. This approach aims to alleviate the scarcity of reliable data for CAD systems in veterinary medicine. This study utilizes datasets comprising cardiomegaly radiographic image data and chronic kidney disease ultrasound image data. After removing annotations and standardizing images, we employed a framework for data augmentation, which consists of a data generation phase and a query phase for filtering the generated data. The experimental results revealed that as the data generated through this framework was added to the training data of the generative model, the frechet inception distance decreased from 84.14 to 50.75 in the radiographic image and from 127.98 to 35.16 in an ultrasound image. Subsequently, when the generated data were incorporated into the training of the classification model, the true negative of the confusion matrix also improved from 0.16 to 0.66 on the radiograph and from 0.44 to 0.64 on the ultrasound image. The proposed framework has the potential to address the challenges of data scarcity in medical CAD, contributing to its advancement.

关键词： Artificial intelligence generative model active learning variational autoencoder veterinary medicine

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An Improved variational autoencoder-Based Clustering Method for Pan-Cancer Diagnosis and Subtyping 1

引用

19th International Conference on Advanced Intelligent Computing Technology and Applications (ICIC)

作者： Tang, Binhua Nie, Jiafei Hohai Univ CIST Epigenet & Funct Grp Nanjing 213002 Jiangsu Peoples R China

ISBN: (数字)9789819947492

ISBN: (纸本)9789819947485;9789819947492

Clustering studies of pan-cancer omics profiling contribute to decipher tumor heterogeneity, and improve diagnosis and tumor subtyping identification. Recently, deep learning clustering methods have developed with powerful representation learning capability, and achieve significant performance improvements. However, the mining capability of deep clustering methods has been hindered by the limited tumor samples. Thus, here we propose a Transformer-based Gaussian mixture variational autoencoder clustering model, which utilizes a Transformer module that can retrieve more efficient and clustering-friendly embedding representation from the reweighting of hidden layer features. We evaluate the performance of the proposed model on three types of profiling omics data, mRNA, miRNA and lncRNA, and perform quantitative comparisons with other typical clustering algorithms. The experiments demonstrate the proposed method has stable and excellent clustering performance, especially in miRNA expression data, with an ACC of 0.9763. In conclusion, with the Transformer module embedded into the proposed deep clustering model, it not only significantly improves the overall performance, but provides novel insights into the reliable tumor diagnosis and subtyping studies.

关键词： Tumor Diagnosis Clustering Transformer 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...

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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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