Výsledky vyhledávání - sequential variational autoencoder

Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement Autor Haga, Takeshi, Kera, Hiroshi, Kawamoto, Kazuhiko

“…In this paper, we propose a sequential variational autoencoder for video disentanglement, which is a representation learning method that can be used to separately extract static and dynamic features from videos…”
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Medical Data Wrangling With Sequential Variational Autoencoders Autor Barrejon, Daniel, Olmos, Pablo M., Artes-Rodriguez, Antonio

“… collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs…”
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Dam anomaly assessment based on sequential variational autoencoder and evidence theory Autor Shu, Xiaosong, Bao, Tengfei, Xu, Ruichen, Li, Yangtao, Zhang, Kang

“…Considering the multi-sources, heterogeneity and complexity of dam anomaly assessment, a novel anomaly assessment model based on sequential variational autoencoder and evidence theory is proposed…”
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DistVAE: Distributed Variational Autoencoder for sequential recommendation Autor Li, Li, Xiahou, Jianbing, Lin, Fan, Su, Songzhi

“… Recently, the generative methods based on Variational Autoencoder (VAE) have shown promising performance in modeling temporal dependencies among items in user sequences…”
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Disentangled Sequential Variational Autoencoder for Collaborative Filtering Autor Wu, Mei-lin, Huang, Jia-jin, Qin, Jin

“… characteristics.In this paper, a variational autoencoder based framework DSVAECF is proposed to disentangle the static and dynamic factors from user's historical…”
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Sensing anomaly of photovoltaic systems with sequential conditional variational autoencoder Autor Li, Ding, Zhang, Yufei, Yang, Zheng, Jin, Yaohui, Xu, Yanyan

“… This study proposes the Sequential Conditional Variational Autoencoder (SCVAE), which can cope with the sequential impacts of the environment on PV…”
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Interpretable data-augmented adversarial variational autoencoder with sequential attention for imbalanced fault diagnosis Autor Liu, Yunpeng, Jiang, Hongkai, Yao, Renhe, Zhu, Hongxuan

“… In this paper, an interpretable data-augmented adversarial variational autoencoder with sequential attention (AVAE-SQA…”
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Robust Synthetic Data Generation for Sequential Financial Models Using Hybrid Variational Autoencoder–Markov Chain Monte Carlo Architectures Autor Bruni Prenestino, Francesco, Barbierato, Enrico, Gatti, Alice

“… This study proposes a novel hybrid architecture that combines variational autoencoders (VAEs) with Markov Chain Monte Carlo…”
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Sequential Variational Autoencoder for Industrial Inferential Modeling Autor Song, Zehui, Wang, Zhuocheng, Yu, Guo, Jiang, Chao, Li, Jun, Bo, Cuimei

“… To address these challenges, this study introduces the Sequential Variational Autoencoder (SVAE). The SVAE model incorporates sophisticated self-attention…”
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Dual Sequential Variational Autoencoders for Fraud Detection

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Disentangled Representation of Longitudinal Β-Amyloid for AD Via Sequential Graph Variational Autoencoder with Supervision Autor Yang, Fan, Wu, Guorong, Hwa Kim, Won

“… We present a novel variational autoencoder model to learn a latent population-level representation of neurodegeneration process based on the longitudinal β…”
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Performing sequential forward selection and variational autoencoder techniques in soil classification based on laser-induced breakdown spectroscopy Autor Harefa, Edward, Zhou, Weidong

“… The dimensions of the data were then reduced using variational autoencoder (VAE), truncated singular value decomposition…”
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S-HR-VQVAE: Sequential Hierarchical Residual Learning Vector Quantized Variational Autoencoder for Video Prediction Autor Adiban, Mohammad, Stefanov, Kalin, Siniscalchi, Sabato Marco, Salvi, Giampiero

“… (AST-PM). We refer to this approach as a sequential hierarchical residual learning vector quantized variational autoencoder (S-HR-VQVAE…”
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Medical data wrangling with sequential variational autoencoders Autor Barrejón, Daniel, Olmos, Pablo M, Artés-Rodríguez, Antonio

“… collected in a misaligned uneven fashion, among other causes. This paper proposes to model medical data records with heterogeneous data types and bursty missing data using sequential variational autoencoders (VAEs…”
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Contrastive Intent-Disentangled Variational AutoEncoder for Sequential Recommendation Autor Yuan, Yafan, Liu, Zhen, Yang, Xinxin, Lu, Sibo

“… We propose Contrastive Intent-Disentangled Variational AutoEncoder (CIDVAE), a novel method to model and disentangle complex latent intentions in Sequential Recommendation (SR…”
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Contrastively Disentangled Sequential Variational Autoencoder Autor Bai, Junwen, Wang, Weiran, Gomes, Carla

“… We propose a novel sequence representation learning method, named Contrastively Disentangled Sequential Variational Autoencoder (C-DSVAE…”
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StressFADS: Learning Latent Autonomic Factors of Stress in the Context of Trauma Recall and Neuromodulation Autor Gazi, Asim H., Chan, Michael, Hsu, Hao-Lun, Bremner, J. Douglas, Rozell, Christopher J., Inan, Omer T.

“…Physiological markers of stress and neuromodulation (e.g., heart rate variability) are often inconsistent when it comes to quantifying changes in autonomic…”
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Semisupervised Text Classification by Variational Autoencoder Autor Xu, Weidi, Tan, Ying

“… In this paper, a novel model, the semisupervised sequential variational autoencoder (SSVAE), is proposed to tackle this problem…”
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Discriminative Feature Extraction Based on Sequential Variational Autoencoder for Speaker Recognition Autor Yoshimura, Takenori, Koike, Natsumi, Hashimoto, Kei, Oura, Keiichiro, Nankaku, Yoshihiko, Tokuda, Keiichi

“…This paper presents an extended version of the variational autoencoder (VAE) for sequence modeling…”
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ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendation Autor Wang, Yu, Zhang, Hengrui, Liu, Zhiwei, Yang, Liangwei, Yu, Philip S

“…) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of Variational AutoEncoders to address these challenges simultaneously…”
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