Search Results - Sparse Approximate Variational Autoencoder

Sparse-Coding Variational Autoencoders by Geadah, Victor, Barello, Gabriel, Greenidge, Daniel, Charles, Adam S, Pillow, Jonathan W

“…) fitting relied on approximate inference methods that ignored uncertainty. Although subsequent work has developed several methods to overcome these obstacles, we propose a novel solution inspired by the variational autoencoder (VAE) framework…”
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Robust Multiple-Measurement Sparsity-Aware STAP with Bayesian Variational Autoencoder by Zhang, Chenxi, Zhao, Huiliang, Chen, Wenchao, Chen, Bo, Wang, Penghui, Jia, Changrui, Liu, Hongwei

“…−time adaptive processing (STAP) often suffers remarkable performance degradation in the heterogeneous clutter environment. Sparse recovery (SR…”
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A High-Generalization Variational Denoising Autoencoder for Micronewton Thrust Signal Noise Removal and Step Reconstruction by Chen, Xingyu, Zhao, Liye, Xu, Jiawen, Liu, Zhikang, Dai, Zhuoping, Xu, Luxiang, Guo, Ning, Zhang, Hong

“… In this study, we have developed a novel generative denoising method, named variational denoising autoencoder (VDAE…”
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Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering by Yuan, Jin, Zhao, He, Liu, Ming, Zhu, Ye, Du, Lan, Gao, Longxiang, Zhang, He, Li, Yunfeng

“… Variational autoencoders (VAE) can address this issue by capturing complex mappings between the posterior parameters and the data…”
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Scalable Weibull Graph Attention Autoencoder for Modeling Document Networks by Wang, Chaojie, Liu, Xinyang, Wang, Dongsheng, Zhang, Hao, Chen, Bo, Zhou, Mingyuan

“…Although existing variational graph autoencoders (VGAEs) have been widely used for modeling and generating graph-structured data, most of them…”
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A semi-supervised temporal modeling strategy integrating VAE and Wasserstein GAN under sparse sampling constraints by Hu, Yujie, Xie, Changrui, Chen, Xi

“… To address this issue, a semi-supervised modeling strategy based on Variational Autoencoder (VAE…”
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Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions by Tang, Tengda, Yao, Jianhua, Wang, Yixian, Sha, Qiuwu, Feng, Hanrui, Xu, Zhen

“… By combining Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE), the algorithm is designed to detect abnormal behaviors in financial transactions…”
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A Comparative Analysis of Data Synthesis Techniques to Improve Classification Accuracy of Raman Spectroscopy Data by Flanagan, Aaron R, Glavin, Frank G

“… Open-source data are difficult to obtain and often sparse; furthermore, the collecting and curating of new spectra require expertise and resources…”
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Machine learning : a Bayesian and optimization perspective by Theodoridis, Sergios

“…/Bayesian learning, as well as short courses on sparse modeling, deep learning, and probabilistic graphical models…”
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Making Model Aware: Pattern Recognition and Analysis in Environmental and Healthcare Data With Machine Learning Models by Jiang, Ziyang

“…Discovering intrinsic patterns in environmental and healthcare data is often very helpful for analyzing correlations and causations among different variables…”
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Inferential Gans and Deep Feature Selection with Applications by Chen, Yao

“… In unsupervised learning, variational autoencoders (VAEs) and generative adverarial networks (GANs) are two most popular and successful generative models…”
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One-Bit Compressed Sensing in the Presence of Noise by Kafle, Swatantra

“…) for signal reconstruction and parameter estimation. We first consider the problem of joint sparse support estimation with one-bit measurements in a distributed setting…”
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Advances in Machine Learning: Nearest Neighbour Search, Learning to Optimize and Generative Modelling by Li, Ke

“…Machine learning is the embodiment of an unapologetically data-driven philosophy that has increasingly become one of the most important drivers of progress in…”
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A Non-negative VAE:the Generalized Gamma Belief Network by Duan, Zhibin, Wen, Tiansheng, Wang, Muyao, Chen, Bo, Zhou, Mingyuan

“… Its notable capability to acquire interpretable latent factors is partially attributed to sparse and non-negative gamma-distributed latent variables…”
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SIReN-VAE: Leveraging Flows and Amortized Inference for Bayesian Networks by Mouton, Jacobie, Kroon, Steve

“…Initial work on variational autoencoders assumed independent latent variables with simple distributions…”
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