Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data | Paper Citation Record

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Unresolved cited work

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Variational autoencoders: A hands-off approach to volatility

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Generating Sentences from a Continuous Space

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Gaussian process prior variational autoencoders

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data National environmental public health tracking network data explorer - asthma in adults, Nov 2017

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Converse, Jeff Hajewski, and Suely Oliveira

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Tutorial on Variational Autoencoders

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Variational Recurrent Auto-Encoders

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Matrix variate distributions

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Hancock and Taghi M

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data beta-V AE: Learning basic visual concepts with a constrained variational framework

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Reducing the dimensionality of data with neural networks

Reference 14

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Scalable gaussian process variational autoencoders

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Jolliffe

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This paper cites Airbnb price prediction using machine learning and sentiment analysis, 2019.

Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Airbnb price prediction using machine learning and sentiment analysis, 2019

Reference 17

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Auto-Encoding Variational Bayes

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Kingma and Max Welling

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This paper cites Probabilistic non-linear principal component analysis with gaussian process latent variable models.

Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Probabilistic non-linear principal component analysis with gaussian process latent variable models

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Deep learning face attributes in the wild

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Auxiliary deep generative models

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data McCulloch, Shayle R

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Tidy tuesday: A weekly data project aimed at the r ecosystem, 2022

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Multi-source social feedback of online news feeds

Reference 25

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Us census demographic data, Mar 2019

Reference 26

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Deep variational autoencoder for modeling functional brain networks and adhd identification

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data A unifying view of sparse approxi- mate gaussian process regression

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Hierarchical variational models

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Used cars dataset - vehicles listings from craigslist.org, 2020

Reference 32

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Variational Autoencoders Pursue PCA Directions (by Accident)

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Rossmann store sales, 2016

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Nonlinear component analysis as a kernel eigenvalue problem

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Variance components

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Using random effects to account for high-cardinality categorical features and repeated measures in deep neural networks

Reference 37

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Integrating Random Effects in Deep Neural Networks

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Uk biobank: An open access resource for identifying the causes of a wide range of complex diseases of middle and old age

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Tipping and Christopher M

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Bayesian deep net glm and glmm

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Recent advances in variational autoencoders with represen- tation learning for biomedical informatics: A survey

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Kim, and Vikas Singh

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This paper cites High-cardinality categorical features include artist ( q = 10K), al- bum (q = 22K), playlist (q = 2.3K) and subgenre (q = 553).

Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data High-cardinality categorical features include artist ( q = 10K), al- bum (q = 22K), playlist (q = 2.3K) and subgenre (q = 553)

Reference 47

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Time-varying features include gender, age, height, different food intakes, smoking habits and many more

Reference 48

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Unresolved cited work

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data wears glasses

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data URL http://www.jstor.org/stable/2680726

Reference 1999

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Unresolved cited work

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Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data Unresolved cited work

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