An underwater acoustic (UWA) channel model with high validity and re-usability is widely demanded. In this paper, we propose a variational auto-encoder (VAE)-based deep generative model which learns an abstract repres...
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ISBN:
(纸本)9781450395625
An underwater acoustic (UWA) channel model with high validity and re-usability is widely demanded. In this paper, we propose a variational auto-encoder (VAE)-based deep generative model which learns an abstract representation of the UWA channel impulse responses (CIRs) and can generate CIR samples with similar features. A customized training process is proposed to avoid the model collapse and being trapped in a gradient pit. The proposed deep generative model is validated using field experimental data sets.
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too compl...
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ISBN:
(纸本)9781450383509
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions. In cases where the domain's factors of variation are unknown or too complex to encode manually, generative models can provide a learned latent space to approximate these factors. When used as a search space, however, the range and diversity of possible outputs are limited to the expressivity and generative capabilities of the learned model. We compare the output diversity of a quality diversity evolutionary search performed in two different search spaces: 1) a predefined parameterized space and 2) the latent space of a variational autoencoder model. We find that the search on an explicit parametric encoding creates more diverse artifact sets than searching the latent space. A learned model is better at interpolating between known data points than at extrapolating or expanding towards unseen examples. We recommend using a generative model's latent space primarily to measure similarity between artifacts rather than for search and generation. Whenever a parametric encoding is obtainable, it should be preferred over a learned representation as it produces a higher diversity of solutions.
Network intrusion detection is one of the most import tasks in today's cyber-security defence applications. In the field of unsupervised learning methods, variants of variational autoencoders promise good results....
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ISBN:
(纸本)9789897584916
Network intrusion detection is one of the most import tasks in today's cyber-security defence applications. In the field of unsupervised learning methods, variants of variational autoencoders promise good results. The fact that these methods are very computationally time-consuming is hardly considered in the literature. Therefore, we propose a new two-stage approach combining a fast preprocessing or filtering method with a variational autoencoder using reconstruction probability. We investigate several types of anomaly detection methods mainly based on autoencoders to select a pre-filtering method and to evaluate the performance of our concept on two well established datasets.
Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a large number of individual cells at a high resolution. These data usually contain measurements of gene expression for many g...
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ISBN:
(纸本)9789813279827;9789813279810
Single-cell RNA sequencing (scRNA-seq) is a powerful tool to profile the transcriptomes of a large number of individual cells at a high resolution. These data usually contain measurements of gene expression for many genes in thousands or tens of thousands of cells, though some datasets now reach the million-cell mark. Projecting high-dimensional scRNA-seq data into a low dimensional space aids downstream analysis and data visualization. Many recent preprints accomplish this using variational autoencoders (VAE), generative models that learn underlying structure of data by compress it into a constrained, low dimensional space. The low dimensional spaces generated by VAEs have revealed complex patterns and novel biological signals from large-scale gene expression data and drug response predictions. Here, we evaluate a simple VAE approach for gene expression data, Tybalt, by training and measuring its performance on sets of simulated scRNA-seq data. We find a number of counter-intuitive performance features: i.e., deeper neural networks can struggle when datasets contain more observations under some parameter configurations. We show that these methods are highly sensitive to parameter tuning: when tuned, the performance of the Tybalt model, which was not optimized for scRNA-seq data, outperforms other popular dimension reduction approaches - PCA, ZIFA, UMAP and t-SNE. On the other hand, without tuning performance can also be remarkably poor on the same data. Our results should discourage authors and reviewers from relying on self-reported performance comparisons to evaluate the relative value of contributions in this area at this time. Instead, we recommend that attempts to compare or benchmark autoencoder methods for scRNA-seq data be performed by disinterested third parties or by methods developers only on unseen benchmark data that are provided to all participants simultaneously because the potential for performance differences due to unequal parameter tuning is so h
Deep learning models have been proved to outperform shallow methods for industrial process fault detection because of their high capacity for complex nonlinearity. However, typical deep models applied to monitoring pr...
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ISBN:
(纸本)9784888983006
Deep learning models have been proved to outperform shallow methods for industrial process fault detection because of their high capacity for complex nonlinearity. However, typical deep models applied to monitoring processes are conducted in a deterministic manner. They are unable to provide a confidence level for each decision. Also, most deep learning methods often need to integrate prior conditions, such as orthogonal latent variables, constraints, and some given distributions. The consequences of these issues cause lots of trials and errors as conventional deep models are built based on experiences. In this paper, a variational auto-encoder is used to set up a framework to tackle these problems. The learned latent variables, which would be orthogonal to each other, are constrained under the specified and optimized objective. Simultaneously, considering uncertainty in data, probability density estimates of latent variables and residuals instead of point estimates are given to design distribution based monitoring indices. A numerical example validates the effectiveness of the proposed method.
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.
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