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检索条件"主题词=variational autoencoder"

共 1532 条记录，以下是301-310 订阅

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Cryptocurrency Price Forecasting using variational autoencoder with Versatile Quantile Modeling 24

Cryptocurrency Price Forecasting using Variational Autoencod...

引用

33rd ACM International Conference on Information and Knowledge Management (CIKM)

作者： Hong, Sungchul An, Seunghwan Jeon, Jong-June Univ Seoul Dept Stat Seoul South Korea Univ Seoul Dept Stat Data Sci Seoul South Korea

ISBN: (纸本)9798400704369

In recent years, there has been a growing interest in probabilistic forecasting methods that offer more comprehensive insights by considering prediction uncertainties rather than point estimates. This paper introduces a novel variational autoencoder learning framework for multivariate distributional forecasting. Our approach employs distributional learning to directly estimate the cumulative distribution function of future time series conditional distributions using the continuous ranked probability score. By incorporating a temporal structure within the latent space and utilizing versatile quantile models, such as the generalized lambda distribution, we enable distributional forecasting by generating synthetic time series data for future time points. To assess the effectiveness of our method, we conduct experiments using a multivariate dataset of real cryptocurrency prices, demonstrating its superiority in forecasting high-volatility scenarios.

关键词： Cryptocurrency Forecasting Time Series Forecasting variational autoencoder Distributional Learning

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Automatic Mask Detecion using Convolutional Neural Networks and variational autoencoder 15

Automatic Mask Detecion using Convolutional Neural Networks ...

引用

IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI)

作者： Silabela, Mxolisi Bogdandy, Bence Toth, Zsolt Eszterhazy Karoly Univ Fac Informat Eger Hungary

ISBN: (纸本)9781728195438

The importance of proper hygienical behaivour is essential in today's word especially during an ongoing pandemic. Wearing mask became mandatory in many countries during the COVID-19 Pandemic. Recognizing whether people are wearing masks is complicated image recognition task which could be facilitated and automated with machine learning techniques. Camera streams are widely available in indoor environments which can be used for object detection and image processing. Convolutional Neural Networks have been successfully applied in image classification and object recognition task in various application areas. There are already trained and openly available general purpose convolutional neural networks which can be used as an initial version for specific applications. A number of different image datasets are also available for research and industrial purposes. The InceptionV3 Neural Network architecture was used to tailored to determine whether a mask is being worn or not using transfer learning techniques, and convolutional neural networks. A variational autoencoder has also been trained to normalize the dataset with respect to skin colour, angle of the head and among other parameters. This paper describes the implementation of a mask recognition software using transfer learning, a convolutional neural network and a variational autoencoder.

关键词： Machine Learning Feedforward neural networks variational autoencoder Image Recognition Deep learning

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VAEPP: variational autoencoder with a Pull-Back Prior 27th

VAEPP: Variational Autoencoder with a Pull-Back Prior

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27th International Conference on Neural Information Processing

作者： Chen, Wenxiao Liu, Wenda Cai, Zhenting Xu, Haowen Pei, Dan Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China

ISBN: (纸本)9783030638351;9783030638368

Many approaches to training generative models by distinct training objectives have been proposed in the past. variational autoencoder (VAE) is an outstanding model of them based on log-likelihood. In this paper, we propose a novel learnable prior, Pull-back Prior, for VAEs by adjusting the density of the prior through a discriminator that can assess the quality of data. It involves the discriminator from the theory of GANs to enrich the prior in VAEs. Based on it, we propose a more general framework, VAE with a Pull-back Prior (VAEPP), which uses existing techniques of VAEs and WGANs, to improve the log-likelihood, quality of sampling and stability of training. In MNIST and CIFAR-10, the log-likelihood of VAEPP outperforms models without autoregressive components and is comparable to autoregressive models. In MNIST, Fashion-MNIST, CIFAR-10 and CelebA, the FID of VAEPP is comparable to GANs and SOTA of VAEs.

关键词： variational autoencoder Deep generative model Adversarial training

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MULTI-SPEAKER AND MULTI-DOMAIN EMOTIONAL VOICE CONVERSION USING FACTORIZED HIERARCHICAL variational autoencoder

MULTI-SPEAKER AND MULTI-DOMAIN EMOTIONAL VOICE CONVERSION US...

引用

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

作者： Elgaar, Mohamed Park, Jungbae Lee, Sang Wan Korea Adv Inst Sci & Technol Daejeon South Korea Humelo Inc Daejeon South Korea KAIST Inst Artificial Intelligence Daejeon South Korea

ISBN: (纸本)9781509066315

Due to the complexity of emotional features, there has been limited success in emotional voice conversion. One major challenge is that conversion between more than two kinds of emotions often accompanies distortion of voice signal. The factorized hierarchical variational autoencoder (FHVAE) [1] was previously shown to have an ability, called sequence-level regularization, to generate disentangled representations of both sequence-level (such as speaker identity) and segment-level features. This study exploits the FHVAE pipeline to produce disentangled representations of emotion, making it possible to greatly facilitate emotional voice conversion. We propose three versions of algorithms for improving the quality of disentangled representation and audio synthesis. We conducted three mean opinion score (MOS) surveys to assess the performance of our models in terms of 1) speaker's voice preservation, 2) emotion conversion, and 3) audio naturalness.

关键词： Emotional Voice Conversion variational autoencoder Disentangled Representation Style Transfer

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Unsupervised Deep Learning for Fault Detection on Spacecraft Using Improved variational autoencoder

Unsupervised Deep Learning for Fault Detection on Spacecraft...

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Chinese Automation Congress (CAC)

作者： Xiang, Gang Tao, Ran Peng, Yu Tian, Kun Qu, Chen Beihang Univ Natl Key Lab Aerosp Intelligent Control Beijing Aerosp Automat Control Inst Sch Automat Sci & Elect Engn Beijing Peoples R China Beijing Aerosp Automat Control Inst Natl Key Lab Aerosp Intelligent Control Beijing Peoples R China

ISBN: (纸本)9781728176871

Fault detection is important for improving the reliability of spacecraft, ensuring the long-term stable operation, and reducing the economic loss caused by failure. In order to solve the problems such as the large amount of test data, the scarcity of fault data samples and the real-time requirements in the field of spacecraft fault detection, an improved unsuperyised deep learning algorithm based on variational autoencoder (VAE) is proposed. The algorithm adopts Gated Recurrent Unit (GRU) based recurrent neural networks as encoder to automatically extract features of input data, and then uses VAE to learn the correlation features of multiple test data. The proposed network, trained only on the normal training dataset, is a typical unsupervised method which could learn features and reconstruct the data on the training set with a small loss. Once the reconstruction loss of the input data is larger than the pre-set threshold, the corresponding input data is considered as fault data. Experiments show that the proposed method is feasible and can effectively detect faults.

关键词： fault detection gated recurrent unit variational autoencoder deep learning spacecraft

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Laughter synthesis: A comparison between variational autoencoder and autoencoder 5

Laughter synthesis: A comparison between Variational autoenc...

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5th International Conference on Advanced Technologies for Signal and Image Processing (ATSIP)

作者： Mansouri, Nadia Lachiri, Zied Tunis El Manar Univ Signal Image & Informat Technol Res Lab LR SITI Natl Engn Sch Tunis ENIT Tunis Tunisia

ISBN: (数字)9781728175133

ISBN: (纸本)9781728175133

Laughter is one of the most famous non verbal sounds that human produce since birth, it conveys messages about our emotional state. These characteristics make it an important sound that should be studied in order to improve the human-machine interactions. In this paper we investigate the audio laughter generation process from its acoustic features. This suggested process is considered as an analysis-transformation-synthesis benchmark based on unsupervised dimensionality reduction techniques: The standard autoencoder (AE) and the variational autoencoder (VAE). Therefore, the laughter synthesis methodology consists of transforming the extracted high-dimensional log magnitude spectrogram into a low-dimensional latent vector. This latent vector contains the most valuable information used to reconstruct a synthetic magnitude spectrogram that will be passed through a specific vocoder to generate the laughter waveform. We systematically, exploit the VAE to create new sound (speech-laugh) based on the interpolation process. To evaluate the performance of these models two evaluation metrics were conducted: objective and subjective evaluations.

关键词： Laughter audio generation variational autoencoder autoencoder interpolation objective and subjective evaluation

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On a Possible Quantum variational autoencoder Circuit

On a Possible Quantum Variational Autoencoder Circuit

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International Joint Conference on Neural Networks (IJCNN)

作者： Pramanik, Sayantan Chandra, M. Girish TCS Res Pune Maharashtra India

ISBN: (纸本)9780738133669

Generative Models have always attracted the attention of Machine Learning research community;they are useful and also generally harder than their discriminative counterparts. In these models, we would be looking into learning the probability distribution of the input and sampling from that to generate new data samples. Since quantum computing and algorithms are inherently random, they can facilitate a natural framework in this situation. But, getting a suitable gate circuit to achieve the requisite quantum state which by repeated preparation and measurement leads to the sought-after data samples is not trivial. In this paper, we propose a quantum circuit which has a flavor of variational autoencoder with the usual visible and hidden nodes for input data and latent distribution. The encoder portion comprises of a suitably chosen parameterized phase ansatz and Inverse Quantum Fourier Transform blocks. Depending on whether the measurement is carried out on the hidden nodes or not, the decoder circuit, which is just not the inverse of the encoder in our case, is configured. The Kullback-Leibler Divergence is used train the circuit towards the required input distribution. Numerical results presented demonstrate the correct functionality of the approach.

关键词： generative model variational autoencoder quantum circuit inverse quantum fourier transform ansatz kullback-liebler divergence

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Anomaly Detection using variational autoencoder with Spectrum Analysis for Time Series Data 9

Anomaly Detection using Variational Autoencoder with Spectru...

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Joint 9th International Conference on Informatics, Electronics and Vision (ICIEV) / 4th International Conference on Imaging, Vision and Pattern Recognition (ICIVPR)

作者： Yokkampon, Umaporn Chumkamon, Sakmongkon Mowshowitz, Abbe Hayashi, Eiji Kyushu Inst Technol Grad Sch Comp Sci & Syst Engn Fukuoka Japan CUNY City Coll Dept Comp Sci New York NY USA

ISBN: (纸本)9781728193311

Uncertainty is an ever present challenge in life. To meet this challenge in data analysis, we propose a method for detecting anomalies in data. This method, based in part on variational autoencoder, identifies spiking raw data by means of spectrum analysis. Time series data are examined in the frequency domain to enhance the detection of anomalies. In this paper, we have used the standard data sets to validate the proposed method. Experimental results show that the comparison of the frequency domain with the original data for anomaly detection can improve validity and accuracy on all criteria. Therefore, analysis of time series data by combining variational autoencoder and frequency domain spectrum methods can effectively detect anomalies. Contribution- We have proposed an anomaly detection method based on the time series data analysis by combining variational autoencoder and Spectrum analysis, and have benchmarked the method with reference to recent related research.

关键词： Anomaly Detection variational autoencoder Time Series Data

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A supervised subtype differentiation learning for building invariant features of non-small cell lung cancer in a latent space of a variational autoencoder 17

A supervised subtype differentiation learning for building i...

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17th International Symposium on Medical Information Processing and Analysis

作者： Cano, Fabian Cruz-Roa, Angel Alvarez-Jimenez, Charlens Becerra, David Siabatto, Andres Romero, Eduardo Univ Nacl Colombia Sede Bogota Comp Imaging & Med Applicat Lab CIMA LAB Bogota Colombia Univ Llanos GITECX Res Grp Automat Data Driven Analyt Lab AdaLab Villavicencio Meta Colombia

ISBN: (数字)9781510650534

ISBN: (纸本)9781510650534;9781510650527

This work presents a supervised subtype differentiation learning of lung cancer features in a latent space constructed with a variational autoencoder. In such space, complicated patterns are quantified by estimating a differentiation grade of typical encoded features of lung cancer subtypes. Specifically, selected tissue samples of non-small cell lung cancer are mapped to a latent space and a logistic regression model assigns differentiation cancer subtype grade to the embedded tissue samples. The latent representation captures the invariant features of the most representative tissue samples for both well-differentiated adenocarcinoma and squamous cell, and confusing cases of poorly differentiated complex mixtures of tissue patterns subtypes. This approach builds up a subtype differentiation grade of non-small cell lung cancer among complex structures which are fully interpretable and integrable with a pathology workflow. Typical tissue samples of well-differentiated lung cancer subtypes are grouped close in the latent space with high confidence of the differentiation grade, while poorly differentiated tissue samples, with lower confidence of the differentiation grade, are located at other latent space regions. A variational autoencoder (VAE) was trained to learn the latent space representation with training data of representative tissue samples picked from well-differentiated adenocarcinoma (five cases) and squamous cell (five cases) lung cancer subtypes. Validation was performed by selecting six cases for training and evaluating the location in the latent space of tissue samples from four different cases. Two different metrics, MAE and RMSE, estimated the location of these patches with respect to the patches belonging to the six cases. The best model, under a cross validation, achieves an average performance of MAE = (0.072 +/- 0.0004) and RMSE = (0.2654 +/- 0.0019). In addition, for ten different cases (five adenocarcinoma and five of squamous cell), performance

关键词： Lung Cancer Latent Space variational autoencoder Metric Learning Digital Pathology

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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...

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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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