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检索条件"主题词=Variational autoencoder"

共 1532 条记录，以下是341-350 订阅

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A Semi-supervised Learning Based on variational autoencoder for Visual-Based Robot Localization 16th

A Semi-supervised Learning Based on Variational Autoencoder ...

引用

16th CCF Conference on Computer Supported Cooperative Work and Social Computing (ChineseCSCW)

作者： Liang, Kaiyun He, Fazhi Zhu, Yuanyuan Gao, Xiaoxin Wuhan Univ Sch Comp Sci & Technol Wuhan Hubei Peoples R China

ISBN: (纸本)9789811945465;9789811945458

Robot localization, the task of determining the current pose of a robot, is a crucial problem of mobile robotic. Visual-based robot localization, which using only cameras as exteroceptive sensors, has become extremely popular due to the relatively cheap cost of cameras. However, current approaches such as Bayes Filter based methods and Visual Odometry need knowledge of prior location and also rely on the feature points in images. This paper presents a novel semi-supervised learning method based on variational autoencoder (VAE) for visual-based robot localization, which does not rely on the prior location and feature points. Because our method does not need prior knowledge, it also can be used as a correction of dead reckoning. We adopt VAE as an unsupervised learning method to preprocess the environment images, followed by a supervised learning model to learn the mapping between the robot's location and processed images. Therefore, one merit of the proposed approach is that it can adopt any state-of-the-art supervised learning models. Furthermore, this semi-supervised learning scheme is also suitable for improving other supervised learning problems by adding extra unlabeled data to the training data set to solve the problem in a semi-supervised manner. We show that this semi-supervised learning scheme can get a high accuracy for pose prediction using a surprisingly small number of labeled images compared to other machine learning methods.

关键词： Robot localization Semi-supervised learning variational autoencoder Neural network

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BVAE: Behavior-aware variational autoencoder for Multi-Behavior Multi-Task Recommendation 23

BVAE: Behavior-aware Variational Autoencoder for Multi-Behav...

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17th ACM Conference on Recommender Systems (RecSys)

作者： Rao, Qianzhen Liu, Yang Pan, Weike Zhong, Ming Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9798400702419

A practical recommender system should be able to handle heterogeneous behavioral feedback as inputs and has multi-task outputs ability. Although the heterogeneous one-class collaborative filtering (HOCCF) and multi-task learning (MTL) methods has been well studied, there is still a lack of targeted manner in their combined fields, i.e., Multi-behavior Multi-task Recommendation (MMR). To fill the gap, we propose a novel recommendation framework called Behavior-aware variational autoencoder (BVAE), which meliorates the parameter sharing and loss minimization method with the VAE structure to address the MMR problem. Specifically, our BVAE includes behavior-aware semi-encoders and decoders, and a target feature fusion network with a global feature filtering network, while using standard deviation to weigh loss. These modules generate the behavior-aware recommended item list via constructing better semantic feature vectors for users, i.e., from dual perspectives of behavioral preference and global interaction. In addition, we optimize our BVAE in terms of adaptability and robustness, i.e., it is concise and flexible to consume any amount of behaviors with different distributions. Extensive empirical studies on two real and widely used datasets confirm the validity of our design and show that our BVAE can outperform the state-of-the-art related baseline methods under multiple evaluation metrics. The processed datasets, source code, and scripts necessary to reproduce the results can be available at https://***/WitnessForest/BVAE.

关键词： variational autoencoder Heterogeneous One-Class Collaborative Filtering Multi-Behavior Multi-Task Recommendation

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PassRVAE: Improved Trawling Attacks via Recurrent variational autoencoder 24

PassRVAE: Improved Trawling Attacks via Recurrent Variationa...

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3rd International Conference on Cryptography, Network Security and Communication Technology (CNSCT)

作者： Xiao, Yujia Hong Kong Polytech Univ Dept Comp Kowloon Hong Kong Peoples R China Harbin Inst Technol Shenzhen Sch Comp Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9798400716959

The prevalence of offline password guessing attacks, also known as trawling, continues to challenge authentication systems. To quantify the threat posed by trawling, existing strategies leverage deep learning to model password habits and predict likely user password choices. We propose PassRVAE, merging variational autoencoders (VAEs) and Gated Recurrent Unit (GRU) networks to augment the accuracy and efficiency of trawling attacks. We further break down the problem by composition policy to evaluate how models fare against specific types of passwords. We evaluate our solution against state-of-the-art models including PassGAN, VAEPass, and VAE-GPT2, on recent password datasets. PassRVAE demonstrates better overall performance as well as per password composition policy, achieving 21.32% higher accuracy with 109 guesses in the RockYou dataset, and 2.74%.27.46% higher accuracy with 108 guesses in six different policies of 4iQ.

关键词： Password guessing attack trawling offline guessing deep learning variational autoencoder

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A WEIGHTED-VARIANCE variational autoencoder MODEL FOR SPEECH ENHANCEMENT 49

A WEIGHTED-VARIANCE VARIATIONAL AUTOENCODER MODEL FOR SPEECH...

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49th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Goltnakani, Ali Sadeghi, Mostafa Alameda-Pineda, Xavier Serizel, Romain Univ Lorraine LORIA INRIA CNRS F-54000 Nancy France Inria Grenoble Grenoble France Univ Grenoble Alpes Grenoble France

ISBN: (纸本)9798350344868;9798350344851

We address speech enhancement based on variational autoencoders, which involves learning a speech prior distribution in the time-frequency (TF) domain. A zero-mean complex-valued Gaussian distribution is usually assumed for the generative model, where the speech information is encoded in the variance as a function of a latent variable. In contrast to this commonly used approach, we propose a weighted variance generative model, where the contribution of each spectrogram time-frame in parameter learning is weighted. We impose a Gamma prior distribution on the weights, which would effectively lead to a Student's t-distribution instead of Gaussian for speech generative modeling. We develop efficient training and speech enhancement algorithms based on the proposed generative model. Our experimental results on spectrogram auto-encoding and speech enhancement demonstrate the effectiveness and robustness of the proposed approach compared to the standard unweighted variance model.

关键词： Speech enhancement generative model variational autoencoder Student's t-distribution

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GraphVAE: Unveiling Dynamic Stock Relationships with variational autoencoder-based Factor Modeling 24

GraphVAE: Unveiling Dynamic Stock Relationships with Variati...

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

作者： Jia, Yulong Li, Guanxing Zhao, Ganlong Lin, Xiangru Li, Guanbin Sun Yat Sen Univ Guangzhou Peoples R China Shanghai Long Life Quant Investment Co Ltd Shenzhen Peoples R China Univ Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9798400704369

Factor models, originating in finance for asset pricing, are fundamental tools in quantitative investment. Recently, there has been a trend towards adopting more flexible machine learning approaches instead of previous linear models. However, traditional factor models and recent deep learning approaches either overlook the relationships among stocks or rely on static, predefined ones, which hampers their representational power and hinders their ability to dynamically adapt to market changes. To overcome this limitation, we introduce a novel dynamic factor model named GraphVAE. This model leverages temporal adaptive dynamic stock relationship graphs, facilitating improved information transfer among stocks within the dynamic probabilistic factor model. Experimental results on three real stock market datasets demonstrate that our method outperforms various state-of-the-art approaches.

关键词： Stock Recommendation variational autoencoder Graph Neural Network

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Hierarchical Diffusion Teaching-Learning-Based Optimizer with variational autoencoder for Mobile Edge Computing System Optimization 15th

Hierarchical Diffusion Teaching-Learning-Based Optimizer wit...

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15th International Conference on Advances in Swarm Intelligence (ICSI)

作者： Xu, Dian Zhou, MengChu Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China

ISBN: (纸本)9789819771837;9789819771844

Evolutionary computation for addressing high-dimensional expensive problems (HEPs) characterized by both high-dimensional decision variables and resource-intensive evaluations is an important area. In this study, we introduce a novel approach, namely the Hierarchical Diffusion Teaching-learning-based Optimizer with variational autoencoder (HDTOV). Firstly, we employ a variational autoencoder to reduce problem dimensions and facilitate the learning of the optimization process. Secondly, we employ a hierarchical population reconstruction strategy to enhance population diversity. Lastly, to exploit the population more effectively, we implement a diffusion mechanism to prevent premature convergence. The proposed method is validated through experiments on a real-life optimization problem arising from the operation of mobile edge computing systems. The experimental results demonstrate the efficacy and efficiency of HDTOV in addressing HEPs by its outperforming the state of the art.

关键词： High-dimensional expensive problems (HEPs) Mobile edge computing systems autoencoder-embedded evolutionary optimization (AEO) variational autoencoder

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Unsupervised Flight Fault Propagation Analysis Using a variational autoencoder 13

Unsupervised Flight Fault Propagation Analysis Using a Varia...

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IEEE 13th International Conference on Pattern Recognition Systems (ICPRS)

作者： Mladenov, Emanuil Martinez-Garcia, Miguel Zhang, Yu Khan, Shaheryar Patankar, Faizan Univ Loughborough Dept Aeronaut & Automot Engn Loughborough Leics England Amygda Labs Ltd Derby England

ISBN: (纸本)9798350333374

Assessing fault propagation is essential for flight safety and for optimal maintenance scheduling of passenger aircrafts. This paper studies such application by way of latent space representation with variational autoencoder. The proposed method learns a topological representation of the dynamics of a normal flight from real industrial data. The method is validated by showcasing that the trained model can separate among different flight modes, e.g., taxiing, landing, and cruise, etc. Then, through case studies of various anomalous flights, it is shown that the dynamics, and as a consequence the latent space representation, of those flights significantly differ from those of the normal flights. The proposed method can be applied subsequently to analyze fault propagation.

关键词： artificial intelligence variational autoencoder condition monitoring flight safety

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CosmoVAE: variational autoencoder for CMB Image Inpainting

CosmoVAE: Variational Autoencoder for CMB Image Inpainting

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Yi, Kai Guo, Yi Fan, Yanan Hamann, Jan Wang, Yu Guang Univ New South Wales Sch Math & Stat Sydney NSW Australia Univ New South Wales Sch Phys Sydney NSW Australia

ISBN: (数字)9781728169262

ISBN: (纸本)9781728169262

Cosmic microwave background radiation (CMB) is critical to the understanding of the early universe and precise estimation of cosmological constants. Due to the contamination of thermal dust noise in the galaxy, the CMB map that is an image on the two-dimensional sphere has missing observations, mainly concentrated on the equatorial region. The noise of the CMB map has a significant impact on the estimation precision for cosmological parameters. Inpainting the CMB map can effectively reduce the uncertainty of parametric estimation. In this paper, we propose a deep learning-based variational autoencoder CosmoVAE, to restoring the missing observations of the CMB map. The input and output of CosmoVAE are square images. To generate training, validation, and test data sets, we segment the full-sky CMB map into many small images by Cartesian projection. CosmoVAE assigns physical quantities to the parameters of the VAE network by using Fourier coefficients, which are sampled by the angular power spectrum of the Gaussian random field as latent variables. CosmoVAE adopts a new loss function to improve the learning performance of the model, which consists of Li reconstruction loss, Kullback-Leibler divergence between the posterior distribution of encoder network and the prior distribution of latent variables, perceptual loss, and total-variation regularizer. The proposed model achieves state of the art performance for Planck Commander 2018 CMB map inpainting.

关键词： variational autoencoder cosmic microwave background inpainting deep learning convolutional neural networks uncertainty quantification KL-divergence regularization perceptual loss total variation angular power spectrum VGG-16 ImageNet

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DvaDRP: a dual variational autoencoder based on big data for drug response prediction 24

DvaDRP: a dual variational autoencoder based on big data for...

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6th International Conference on Big-data Service and Intelligent Computation (BDSIC)

作者： Liao, Xin Yu, Bin Qingdao Univ Sci & Technol Sch Data Sci Qingdao 266061 Peoples R China

ISBN: (纸本)9798400718069

Precision medicine represents the direction of cancer treatment in the era of big data. Developing effective drug response prediction (DRP) models is crucial for achieving precision medicine. Although some DRP models have been proposed, their generalization performance on external datasets is generally mediocre due to insufficient extraction of drug features. To provide valuable references for clinical treatment, it is necessary for models to learn deeper feature representations from big data. Unlike traditional matrix factorization DRP methods, we propose a dual-variational autoencoder DRP model based on multi-omics. The dual-variational autoencoder obtains deeper latent feature vectors from big data for DRP. The proposed method is validated on GDSC and CCLE datasets. Compared to state-of-the-art prediction methods, the proposed method achieves the best performance in both regression and classification predictions.

关键词： Big data Drug response prediction Precision medicine variational autoencoder

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Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic variational autoencoder 6

Out of Distribution Reasoning by Weakly-Supervised Disentang...

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6th International Conference on System Reliability and Safety (ICSRS)

作者： Rahiminasab, Zahra Yuhas, Michael Easwaran, Arvind Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Energy Res Inst Singapore Singapore

ISBN: (纸本)9781665470926

Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning), are necessary to ensure the safety of results generated by machine learning models. Recently there have been promising results for OOD detection in the latent space of variational autoencoders (VAEs). However, without disentanglement, VAEs cannot perform OOD reasoning. Disentanglement ensures a one-to-many mapping between generative factors of OOD (e.g., rain in image data) and the latent variables to which they are encoded. Although previous literature has focused on weakly-supervised disentanglement on simple datasets with known and independent generative factors. In practice, achieving full disentanglement through weak supervision is impossible for complex datasets, such as Carla, with unknown and abstract generative factors. As a result, we propose an OOD reasoning framework that learns a partially disentangled VAE to reason about complex datasets. Our framework consists of three steps: partitioning data based on observed generative factors, training a VAE as a logic tensor network that satisfies disentanglement rules, and run-time OOD reasoning. We evaluate our approach on the Carla dataset and compare the results against three state-of-the-art methods. We found that our framework outperformed these methods in terms of disentanglement and end-to-end OOD reasoning.

关键词： Out-of-distribution reasoning Weakly-supervised disentanglement variational autoencoder Logic tensor network

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