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检索条件"主题词=variational autoencoder"

共 1535 条记录，以下是211-220 订阅

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Adversarial and Contrastive variational autoencoder for Sequential Recommendation 21

Adversarial and Contrastive Variational Autoencoder for Sequ...

引用

30th World Wide Web Conference (WWW)

作者： Xie, Zhe Liu, Chengxuan Zhang, Yichi Lu, Hongtao Wang, Dong Ding, Yue Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Cyber Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Sch Software Shanghai Peoples R China

ISBN: (纸本)9781450383127

Sequential recommendation as an emerging topic has attracted increasing attention due to its important practical significance. Models based on deep learning and attention mechanism have achieved good performance in sequential recommendation. Recently, the generative models based on variational autoencoder (VAE) have shown the unique advantage in collaborative filtering. In particular, the sequential VAE model as a recurrent version of VAE can effectively capture temporal dependencies among items in user sequence and perform sequential recommendation. However, VAE-based models suffer from a common limitation that the representational ability of the obtained approximate posterior distribution is limited, resulting in lower quality of generated samples. This is especially true for generating sequences. To solve the above problem, in this work, we propose a novel method called Adversarial and Contrastive variational autoencoder (ACVAE) for sequential recommendation. Specifically, we first introduce the adversarial training for sequence generation under the Adversarial variational Bayes (AVB) framework, which enables our model to generate high-quality latent variables. Then, we employ the contrastive loss. The latent variables will be able to learn more personalized and salient characteristics by minimizing the contrastive loss. Besides, when encoding the sequence, we apply a recurrent and convolutional structure to capture global and local relationships in the sequence. Finally, we conduct extensive experiments on four real-world datasets. The experimental results show that our proposed ACVAE model outperforms other state-of-the-art methods.

关键词： Sequential Recommendation variational autoencoder Adversarial Learning Contrastive Learning

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Bilateral variational autoencoder for Collaborative Filtering 21

Bilateral Variational Autoencoder for Collaborative Filterin...

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14th ACM International Conference on Web Search and Data Mining (WSDM)

作者： Quoc-Tuan Truong Salah, Aghiles Lauw, Hady W. Singapore Management Univ Sch Informat Syst Singapore Singapore

ISBN: (纸本)9781450382977

Preference data is a form of dyadic data, with measurements associated with pairs of elements arising from two discrete sets of objects. These are users and items, as well as their interactions, e.g., ratings. We are interested in learning representations for both sets of objects, i.e., users and items, to predict unknown pairwise interactions. Motivated by the recent successes of deep latent variable models, we propose Bilateral variational autoencoder (BiVAE), which arises from a combination of a generative model of dyadic data with two inference models, user- and item-based, parameterized by neural networks. Interestingly, our model can take the form of a Bayesian variational autoencoder either on the user or item side. As opposed to the vanilla VAE model, BiVAE is "bilateral", in that users and items are treated similarly, making it more apt for two-way or dyadic data. While theoretically sound, we formally show that, similarly to VAE, our model might suffer from an over-regularized latent space. This issue, known as posterior collapse in the VAE literature, may appear due to assuming an over-simplified prior (isotropic Gaussian) over the latent space. Hence, we further propose a mitigation of this issue by introducing constrained adaptive prior (CAP) for learning user-and item-dependent prior distributions. Empirical results on several real-world datasets show that the proposed model outperforms conventional VAE and other comparative collaborative filtering models in terms of item recommendation. Moreover, the proposed CAP further boosts the performance of BiVAE. An implementation of BiVAE is available on Cornac recommender library.

关键词： Collaborative Filtering variational autoencoder Dyadic Data

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INVESTIGATION AND COMPARISON OF OPTIMIZATION METHODS FOR variational autoencoder-BASED UNDERDETERMINED MULTICHANNEL SOURCE SEPARATION 47

INVESTIGATION AND COMPARISON OF OPTIMIZATION METHODS FOR VAR...

引用

47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Seki, Shogo Kameoka, Hirokazu Li, Li NTT Corp NTT Commun Sci Labs Tokyo Japan Nagoya Univ Informat Technol Ctr Nagoya Aichi Japan

ISBN: (纸本)9781665405409

In this paper, we investigate two algorithms for variational autoencoder (VAE)-based underdetermined multichannel source separation. We previously extended the multichannel VAE (MVAE) method for determined multichannel source separation and proposed the generalized MVAE (GMVAE) method for underdetermined multichannel source separation. The GMVAE method employs a conditional VAE (CVAE) as the source model representing the power spectrograms of the underlying sources present in a mixture. While we developed a convergence-guaranteed parameter estimation algorithm using a majorization-minimization/minorization-maximization (MM) algorithm, an expectation-maximization (EM) algorithm also allows us to design another algorithm with the same property. However, a comparison of the MM-based and EM-based algorithms has not yet been revealed. To elucidate this, we investigate the MM-based and EM-based algorithms for the GMVAE method, using an improved CVAE variant called auxiliary classifier VAE (ACVAE). The experimental results suggest that the EM-based algorithm takes less computational cost, achieving comparable separation performance with the MM-based algorithm.

关键词： Underdetermined multichannel source separation variational autoencoder convergence-guaranteed algorithm

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Examining the Utility of Differentially Private Synthetic Data Generated using variational autoencoder with TensorFlow Privacy 27

Examining the Utility of Differentially Private Synthetic Da...

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27th IEEE Pacific Rim International Symposium on Dependable Computing (PRDC)

作者： Tai, Bo-Chen Li, Szu-Chuang Huang, Yennun Wang, Pang-Chieh Acad Sinica CITI Taipei Taiwan Tamkang Univ Dept Informat & Commun Taipei Taiwan ITRI ICL Hsinchu Taiwan

ISBN: (纸本)9781665485555

With the emergence of AI(artificial intelligence), it is becoming more and more critical for organizations to utilize it to their advantage. However, organizations that possess a decent amount of data might not have the technical competence to perform machine learning, and vice versa. Hence, it is reasonable for the two kinds of organizations to work together to realize the value of the data. With the increasing concern over data privacy, regulations such as GDPR(General Data Protection Regulation) prevent an organization from sharing data with another unless the data is processed to the point that the individuals in the data are not identifiable. Various ways of data anonymization have been proposed and developed, including the ones that utilize neural networks to achieve the goal, like AE, VAE, and GAN. With the addition of a differential privacy framework like TensorFlow Privacy, privacy can be guaranteed, but data still needs to be usable after privacy protection measures are deployed. The present study aims to integrate TensorFlow Privacy into the synthetic data generation process and evaluate its usefulness for daily use in the industries. Since TensorFlow Privacy brings a provable privacy guarantee to synthetic data, the present study focuses on the evaluation of data utility. TensorFlow is widely used for machine learning in the industry and academically. TensorFlow Privacy, which is also developed by Google, can prove to be a valuable addition to the synthetic data generation pipeline. The result shows that VAE with TensorFlow Privacy 1) generates synthetic data with good data utility in most cases in terms of descriptive statistics and machine learning classification tasks, and 2) The customizable TensorFlow Privacy parameters work as intended in terms of privacy-utility trade-off.

关键词： synthetic data differential privacy variational autoencoder

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A SEMI-SUPERVISED APPROACH FOR IDENTIFYING ABNORMAL HEART SOUNDS USING variational autoencoder

A SEMI-SUPERVISED APPROACH FOR IDENTIFYING ABNORMAL HEART SO...

引用

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

作者： Banerjee, Rohan Ghose, Avik Tata Consultancy Serv Res & Innovat Mumbai Maharashtra India

ISBN: (纸本)9781509066315

Abnormal heart sounds may have diverse frequency characteristics depending upon underlying pathological conditions. Designing a binary classifier for predicting normal and abnormal heart sounds using supervised learning requires a lot of training data, covering different types of cardiac abnormalities. In this paper, we propose a semi-supervised approach to solve the problem. A convolutional variational autoencoder (VAE) structure is defined for learning the probability distribution of the spectrogram properties of normal heart sounds. The Kullback-Leibler (KL) divergence between the known prior distribution of the VAE and the encoded distribution is taken as an anomaly score for detecting abnormal heart sounds. The proposed approach is evaluated on open access and in-house datasets of Phonocardiogram (PCG) signals, recorded from normal subjects and patients, having cardiovascular diseases, cardiac murmurs and extra heart sounds. Results show that an improved classification performance is achieved in comparison to the existing approaches.

关键词： Heart sounds variational autoencoder Semi-supervised learning Convolutional Neural Network

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Convolutional One-Dimensional variational autoencoder Based Intrusion detection 19

Convolutional One-Dimensional Variational Autoencoder Based ...

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6th IEEE Cyber Science and Technology Congress (CyberSciTech)

作者： Wu, Kailin Cao, MengTing Wang, Pan Wang, ZiXuan Nanjing Univ Posts & Telecommun Sch Modern Posts Nanjing Peoples R China

ISBN: (纸本)9781665421744

Intrusion detection technology is an important part of network security. It detects various intrusions by collecting and analyzing various information on the network, which is the focus of maintaining network *** the popularization of the network and the increase of the network rate, the network attack behavior is increasing and the attack methods are constantly updated. The traditional detection technology can not meet the demand of the new network environment for network *** view of the defects of the existing intrusion detection algorithm model based on deep learning, such as long training time, more hyperparameters and high data demand, it is feasible to detect malicious traffic by learning the characteristics of normal traffic. In this paper, a variational self-encoder (COD-VAE) intrusion detection model based on one-dimensional convolutional neural network is proposed, which can identify malicious traffic more *** intrusion detection algorithm is used to detect the intrusion of the intrusion detection *** results show that all the indexes of the algorithm are higher than the comparison algorithm, which verifies the effectiveness of the algorithm.

关键词： Intrusion detection Unsupervised Machine learning Deep learning variational autoencoder

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A Transformer-Based variational autoencoder for Sentence Generation

A Transformer-Based Variational Autoencoder for Sentence Gen...

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International Joint Conference on Neural Networks (IJCNN)

作者： Liu, Danyang Liu, Gongshen Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9781728119854

The variational autoencoder(VAE) has been proved to be a most efficient generative model, but its applications in natural language tasks have not been fully developed. A novel variational autoencoder for natural texts generation is presented in this paper. Compared to the previously introduced variational autoencoder for natural text where both the encoder and decoder are RNN-based, we propose a new transformer-based architecture and augment the decoder with an LSTM language model layer to fully exploit information of latent variables. We also propose some methods to deal with problems during training time, such as KL divergency collapsing and model degradation. In the experiment, we use random sampling and linear interpolation to test our model. Results show that the generated sentences by our approach are more meaningful and the semantics are more coherent in the latent space.

关键词： variational autoencoder text generation self-attention transformer

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Stellar Cluster Detection using GMM with Deep variational autoencoder

Stellar Cluster Detection using GMM with Deep Variational Au...

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IEEE Conference on Recent Advances in Intelligent Computational Systems (RAICS)

作者： Karmakar, Arnab Mishra, Deepak Tej, Anandmayee Indian Inst Space Sci & Technol Dept Avion Trivandrum Kerala India Indian Inst Space Sci & Technol Dept Earth Ans Space Sci Trivandrum Kerala India

ISBN: (纸本)9781538673362

Detecting stellar clusters have always been an important research problem in Astronomy. Although images do not convey very detailed information in detecting stellar density enhancements, we attempt to understand if new machine learning techniques can reveal patterns that would assist in drawing better inferences from the available image data. This paper describes an unsupervised approach in detecting star clusters using Deep variational autoencoder combined with a Gaussian Mixture Model. We show that our method works significantly well in comparison with state-of-the-art detection algorithm in recognizing a variety of star clusters even in the presence of noise and distortion.

关键词： Deep Learning variational autoencoder Gaussian Mixture Model Star Cluster Astronomy

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Reconstructing Neutral Face Expressions with Disentangled variational autoencoder 40th

Reconstructing Neutral Face Expressions with Disentangled Va...

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40th Computer Graphics International Conference (CGI)

作者： Wiem, Grina Ali, Douik Univ Sousse Sousse Technol Ctr Natl Sch Engineers Sousse Networked Objects Control & Commun Syst Lab Sahloul Belt Rd Sousse 4054 Tunisia Univ Monastir Natl Engn Sch Monastir Rue Ibn Jazzar Monastir 5035 Tunisia

ISBN: (纸本)9783031500718;9783031500725

This study tackles unsupervised learning for disentangled representations in facial expression generation. It introduces a novel deep architecture by combining FactorVAE and beta-VAE concepts, incorporating the Ranger optimizer and Dropout layers. The goal is to learn disentangled representations that capture essential facial expression factors, like the neutral expression, while ensuring independence between different expression dimensions. This approach achieves faster convergence and improved optimization, striking a better balance between disentanglement and reconstruction quality. The method enables accurate and diverse facial expression generation and enhances model generalization through adaptive learning rate adjustments. The learned disentangled representations also facilitate the generation of realistic and interpretable facial expressions, making it a promising approach for various facial expression generation tasks.

关键词： Face expression generation Disentangled representation variational autoencoder Ranger optimizer

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LSTMVAEF: Vivid Layout via LSTM-Based variational autoencoder Framework 16th

LSTMVAEF: Vivid Layout via LSTM-Based Variational Autoencode...

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16th IAPR International Conference on Document Analysis and Recognition (ICDAR)

作者： He, Jie Wu, Xingjiao Hu, Wenxin Yang, Jing East China Normal Univ Shanghai Peoples R China

ISBN: (纸本)9783030863319

The lack of training data is still a challenge in the Document Layout Analysis task (DLA). Synthetic data is an effective way to tackle this challenge. In this paper, we propose an LSTM-based variational autoencoder framework (LSTMVAF) to synthesize layouts for DLA. Compared with the previous method, our method can generate more complicated layouts and only need training data from DLA without extra annotation. We use LSTM models as basic models to learn the potential representing of class and position information of elements within a page. It is worth mentioning that we design a weight adaptation strategy to help model train faster. The experiment shows our model can generate more vivid layouts that only need a few real document pages.

关键词： Document Layout Analysis variational autoencoder Document generation

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