Search Results - Relevance factor variational autoencoder

Developing a fair and interpretable representation of the clock drawing test for mitigating low education and racial bias by Zhang, Jiaqing, Bandyopadhyay, Sabyasachi, Kimmet, Faith, Wittmayer, Jack, Khezeli, Kia, Libon, David J., Price, Catherine C., Rashidi, Parisa

“…The clock drawing test (CDT) is a neuropsychological assessment tool to screen an individual’s cognitive ability. In this study, we developed a Fair and…”
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Explainable semi-supervised deep learning shows that dementia is associated with small, avocado-shaped clocks with irregularly placed hands by Bandyopadhyay, Sabyasachi, Wittmayer, Jack, Libon, David J., Tighe, Patrick, Price, Catherine, Rashidi, Parisa

“… In this study, we used the relevance factor variational autoencoder (RF-VAE), a deep generative neural network, to represent digitized clock drawings from multiple institutions using an optimal number of disentangled latent factors…”
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A vision transformer approach for fully automated and scalable dementia screening using clock drawing test images by Bone, Michael B., Freedman, Morris, Black, Sandra E., Felsky, Daniel, Kumar, Sanjeev, Pugh, Bradley, Strother, Stephen C., Tang‐Wai, David F., Tartaglia, Maria Carmela, Buchsbaum, Bradley R.

“…‐scored features (74.3%) and existing deep learning models (MiniVGG = 73.3%, MobileNetV2 = 72.3%, relevance factor variational autoencoder…”
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ARD-VAE: A Statistical Formulation to Find the Relevant Latent Dimensions of Variational Autoencoders by Saha, Surojit, Joshi, Sarang, Whitaker, Ross

“…The variational autoencoder (VAE) [19], [41] is a popular, deep, latent-variable model (DLVM…”
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Visualizing the dynamic change of Ocular Response Analyzer waveform using Variational Autoencoder in association with the peripapillary retinal arteries angle by Asano, Shotaro, Asaoka, Ryo, Yamashita, Takehiro, Aoki, Shuichiro, Matsuura, Masato, Fujino, Yuri, Murata, Hiroshi, Nakakura, Shunsuke, Nakao, Yoshitaka, Kiuchi, Yoshiaki

“…), using a generative deep learning method of variational autoencoder (VAE). Fifty-four eyes of 52 subjects were enrolled…”
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FaIRClocks: Fair and Interpretable Representation of the Clock Drawing Test for mitigating classifier bias against lower educational groups by Zhang, Jiaqing, Bandyopadhyay, Sabyasachi, Kimmet, Faith, Wittmayer, Jack, Khezeli, Kia, Libon, David J, Price, Catherine C, Rashidi, Parisa

“… We represented clock drawings with a 10-dimensional latent embedding using Relevance Factor Variational Autoencoder (RF-VAE…”
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GF-LRP: A Method for Explaining Predictions Made by Variational Graph Auto-Encoders by Rodrigo-Bonet, Esther, Deligiannis, Nikos

“…Variational graph autoencoders (VGAEs) combine the best of graph convolutional networks (GCNs…”
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Deep learning for the harmonization of structural MRI scans: a survey by Abbasi, Soolmaz, Lan, Haoyu, Choupan, Jeiran, Sheikh-Bahaei, Nasim, Pandey, Gaurav, Varghese, Bino

“… These variations affect data consistency and compatibility across different sources. Image harmonization is a critical step to mitigate the effects of factors like inherent…”
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Enhancing Multi-Turn Dialogue Generation with MoE-Based Multi-Latent Variable Fusion by Cui, Zishun, Sun, Xiao

“… To address the one-to-many mapping problem caused by conversational uncertainty, we propose a novel framework integrating Variational Autoencoder (VAE…”
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Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach by Sheng, Xia, Gui, Yike, Yu, Jie, Wang, Yitian, Li, Zhenghao, Zhang, Xiaoya, Xing, Yuxin, Wang, Yuqing, Li, Zhaojun, Zheng, Mingyue, Yang, Liquan, Li, Xutong

“… Our approach utilizes a variational autoencoder (VAE) generative model integrated with reinforcement learning for multi-objective optimization…”
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Period-aggregated transformer for learning latent seasonalities in long-horizon financial time series by Tang, Zhenyang, Huang, Jinshui, Rinprasertmeechai, Denisa

“…). The model integrates a variational autoencoder (VAE) with a period-to-period attention mechanism for multistep prediction in the financial…”
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Survey on sampling conditioned brain images and imaging measures with generative models by Cheong, Sehyoung, Lee, Hoseok, Kim, Won Hwa

“… These models, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and diffusion models, leverage deep learning to generate high-quality brain images while maintaining biological and clinical relevance…”
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Uncovering Population PK Covariates from VAE-Generated Latent Spaces by Perazzolo, Diego, Castellani, Chiara, Grisan, Enrico

“… Traditional methods may fail to capture hidden patterns within the data. In this study, we propose a data-driven, model-free framework that integrates Variational Autoencoders (VAEs…”
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Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS by Marghi, Yeganeh, Gala, Rohan, Baftizadeh, Fahimeh, Sümbül, Uygar

“…Reproducible definition and identification of cell types is essential to enable investigations into their biological function, and understanding their relevance in the context of development, disease and evolution…”
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Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection by Challu, Cristian, Jiang, Peihong, Ying Nian Wu, Callot, Laurent

“… Among reconstruction-based models, most previous work has focused on Variational Autoencoders and Generative Adversarial Networks…”
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Latent feature disentanglement for 3D meshes by Levinson, Jake, Sud, Avneesh, Makadia, Ameesh

“… In this paper we build upon recently introduced 3D mesh-convolutional Variational AutoEncoders which have shown great promise for learning rich representations of deformable 3D shapes…”
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