Due to the strategic importance of satellites, the safety and reliability of satellites have become more important. Sensors that monitor satellites generate lots of multivariate time series, and the abnormal patterns ...
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Due to the strategic importance of satellites, the safety and reliability of satellites have become more important. Sensors that monitor satellites generate lots of multivariate time series, and the abnormal patterns in the multivariate time series may imply malfunctions. The existing anomaly detection methods for multivariate time series have poor effects when processing the data with few dimensions or sparse relationships between sequences. This paper proposes an unsupervised anomaly detection model based on the variational Transformer to solve the above problems. The model uses the Transformer's self-attention mechanism to capture the potential correlations between sequences and capture the multi-scale temporal information through the improved positional encoding and up-sampling algorithm. Then, the model comprehensively considers the extracted features through the residual variational autoencoder to perform effective anomaly detection. Experimental results on a real dataset and two public datasets show that the proposed method is superior to the mainstream and state-ofthe-art methods.
With the development of e-commerce, payment by credit card has become an essential means for the purchases of goods and services online. Especially, the Manufacturing Sector faces a high risk of fraud online payment. ...
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ISBN:
(纸本)9783030858742;9783030858735
With the development of e-commerce, payment by credit card has become an essential means for the purchases of goods and services online. Especially, the Manufacturing Sector faces a high risk of fraud online payment. Its high turnover is the reason making this sector is lucrative with fraud. This gave rise to fraudulent activity on the accounts of private users, banks, and other services. For this reason, in recent years, many studies have been carried out using machine learning techniques to detect and block fraudulent transactions. This article aims to present a new approach based on real-time data combining two methods for the detection of credit card fraud. We first use the variational autoencoder(VAE) to obtain representations of normal transactions, and then we train a support vector data description (SVDD) model with these representations. The advantage of the representation learned automatically by the variational autoencoder is that it makes the data smoother, which makes it possible to increase the detection performance of one-class classification methods. The performance evaluation of the proposed model is done on real data from European credit cardholders. Our experiments show that our approach has obtained good results with a very high fraud detection rate.
The curse of dimensionality in high-dimensional data makes it difficult to capture the abnormality of data points in full data space. To deal with this problem, we propose an outlier detection model based on Variation...
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ISBN:
(纸本)9781665439022
The curse of dimensionality in high-dimensional data makes it difficult to capture the abnormality of data points in full data space. To deal with this problem, we propose an outlier detection model based on variational autoencoder and Genetic Algorithm for subspace outlier analysis of high-dimensional data (VAGA). The proposed VAGA model constructs a variational autoencoder (VAE) to preliminarily detect outliers. Then the genetic algorithm (GA) is used to search the abnormal subspace of the outliers obtained by the VAE layer to provide a basis for subspace outlier analysis. The subsequent clustering of the abnormal subspaces help filter out the false positives which are fed back to the VAE layer to adjust network weights. The comparative experiments performed on three public benchmark datasets show that the outlier detection results of the proposed VAGA model are highly interpretable and have better accuracy performance than the state-of-the-art outlier detection methods.
Conditional variational autoencoder (cVAE) has shown promising performance in dialogue generation. However, there still exists two issues in dialog cVAE model. The first issue is the Kullback-Leiblier (KL) vanishing p...
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ISBN:
(纸本)9781728176055
Conditional variational autoencoder (cVAE) has shown promising performance in dialogue generation. However, there still exists two issues in dialog cVAE model. The first issue is the Kullback-Leiblier (KL) vanishing problem which results in degenerating cVAE into a simple recurrent neural network. The second issue is the assumption of isotropic Gaussian prior for latent variable which is too simple to assure diversity of the generated responses. To handle these issues, a simple distribution should be transformed into a complex distribution and simultaneously the value of KL divergence should be preserved. This paper presents the dialogue flow VAE (DF-VAE) for variational dialogue generation. In particular, KL vanishing is tackled by a new normalizing flow. An inverse autoregressive flow is proposed to transform isotropic Gaussian prior to a rich distribution. In the experiments, the proposed DF-VAE is significantly better than the other methods in terms of different evaluation metrics. The diversity of generated dialogue responses is enhanced. Ablation study is conducted to illustrate the merit of the proposed flow models.
User behaviour on purchasing is always driven by complex latent factors, which are highly disentangled in the real world. Learning latent factorized representation of users can uncover user intentions behind the obser...
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ISBN:
(纸本)9783030731991;9783030732004
User behaviour on purchasing is always driven by complex latent factors, which are highly disentangled in the real world. Learning latent factorized representation of users can uncover user intentions behind the observed data (i.e. user-item interaction) and improve the robustness and interpretability of the recommender system. However, existing collaborative filtering methods learning disentangled representation face problems of balancing the trade-off between reconstruction quality and disentanglement. In this paper, we propose a controllable variational autoencoder framework for collaborative filtering. Specifically, we adopt a modified Proportional-Integral-Derivative (PID) control to the beta-VAE objective to automatically tune the hyperparameter beta using the output of Kullback-Leibler divergence as feedback. We further introduce item embeddings to guide the system to learn representation related to the real-world concepts using a factorized Gaussian distribution. Experimental results show that our model can get a crucial improvement over state-of-the-art baselines. We further evaluate our model's effectiveness to control the trade-off between reconstruction error and disentanglement quality in the recommendation.
In theory, the variational auto-encoder (VAE) is not suitable for recommendation tasks, although it has been successfully utilized for collaborative filtering (CF) models. In this paper, we propose a Gaussian Copula-V...
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ISBN:
(纸本)9781450384469
In theory, the variational auto-encoder (VAE) is not suitable for recommendation tasks, although it has been successfully utilized for collaborative filtering (CF) models. In this paper, we propose a Gaussian Copula-Vector Quantized autoencoder (GC-VQAE) model that differs prior arts in two key ways: (1) Gaussian Copula helps to model the dependencies among latent variables which are used to construct a more complex distribution compared with the meanfield theory;and (2) by incorporating a vector quantisation method into encoders our model can learn discrete representations which are consistent with the observed data rather than directly sampling from the simple Gaussian distributions. Our approach is able to circumvent the "posterior collapse" issue and break the prior constraint to improve the flexibility of latent vector encoding and learning ability. Empirically, GC-VQAE can significantly improve the recommendation performance compared to existing state-of-the-art methods.
Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated images for each pathology, a strenuo...
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ISBN:
(纸本)9781665412469
Automatic segmentation of brain abnormalities is challenging, as they vary considerably from one pathology to another. Current methods are supervised and require numerous annotated images for each pathology, a strenuous task. To tackle anatomical variability, Unsupervised Anomaly Detection (UAD) methods are proposed, detecting anomalies as outliers of a healthy model learned using a variational autoencoder (VAE). Previous work on UAD adopted 2D approaches, meaning that MRIs are processed as a collection of independent slices. Yet, it does not fully exploit the spatial information contained in MRI. Here, we propose to perform UAD in a 3D fashion and compare 2D and 3D VAEs. As a side contribution, we present a new loss function guarantying a robust training. Learning is performed using a multicentric dataset of healthy brain MRIs, and segmentation performances are estimated on White-Matter Hyperintensities and tumors lesions. Experiments demonstrate the interest of 3D methods which outperform their 2D counterparts.
We present a multi-module framework based on Conditional variational autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model w...
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We present a multi-module framework based on Conditional variational autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normal waveforms and to improve the sensitivity of the model to identify a specific type of fault when we have limited samples for a given module type. We studied several Artificial Neural Network (ANN) architectures for our CVAE model and evaluated the model performance by looking at their loss landscape for stability and generalization. Our results for the Spallation Neutron Source (SNS) experimental data show that the trained model generalizes well to detecting multiple fault types for several HVCM module types. The results of this study can be used to improve the HVCM reliability and overall SNS uptime.
Recommender systems have become indispensable for several Web sites, helping users deal with big amounts of data. They are capable of analyzing user/item interactions taking place on-line, and provide each user with a...
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ISBN:
(纸本)9781450362382
Recommender systems have become indispensable for several Web sites, helping users deal with big amounts of data. They are capable of analyzing user/item interactions taking place on-line, and provide each user with a list of suggestions sorted by relevance. Items with the same or very close relevance, however, may occupy different positions in the ranking and may be exposed to completely different levels of attention. This promotes unfair treatment and can only be addressed by a long term strategy. variational autoencoders (VAEs) were recently proposed as the state-of-the-art for collaborative filtering recommendations, but as every other approach, they generate homogeneous prediction scores among the highest positions. In this paper, we propose incorporating randomness in the regular operation of VAEs in order to increase the fairness (mitigate the position bias) in multiple rounds of recommendation. We argue that adding a noise component when sampling values from VAE's latent representation provides long term fairness, despite of a tolerable decrease in ranking quality (NDCG). We calculate the trade-off between unfairness and NDCG when introducing 4 different noise distributions. The solution has proved to be a very practical one and the results point for a clear positive effect of turning recommendation far more fair, despite some small NDCG loss in Movie Lens, Netflix and MSD datasets. In our best scenario, the unfairness was reduced by 76% despite a decrease of 5% in the quality of ranking.
Traditional approaches to terrain heightmap generation rely on geometric methods to generate a matrix of elevation values using noise functions. More advanced approaches attempt to model the natural processes that sha...
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ISBN:
(纸本)9780738133669
Traditional approaches to terrain heightmap generation rely on geometric methods to generate a matrix of elevation values using noise functions. More advanced approaches attempt to model the natural processes that shape landmasses in the real world, such as wind, moisture, and rainfall. This survey leverages recent advancements in image generation using generative machine learning models in order to evaluate their effectiveness in this problem space. A variational autoencoder (VAE), generative adversarial network (GAN), and PixelCNN network are trained on real-world island heightmap data and produce realistic island terrain. The author compares the results in terms of image quality and "closeness" to the original real images, and evaluates the trade-off between quality and training/generation speed.
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