Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma | Paper Citation Record

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This paper cites Tensorflow: A system for large-scale machine learning.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Tensorflow: A system for large-scale machine learning

Reference 1

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This paper cites Y., Diniz-Filho, A., Costa, V.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Y., Diniz-Filho, A., Costa, V

Reference 2

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This paper cites High-dimensional bayesian geostatistics.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma High-dimensional bayesian geostatistics

Reference 3

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This paper cites E., Thompson, P.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma E., Thompson, P

Reference 4

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This paper cites I., Mukherjee, S., Medeiros, F.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma I., Mukherjee, S., Medeiros, F

Reference 5

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This paper cites I., Mwanza, J.-C., Warren, J.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma I., Mwanza, J.-C., Warren, J

Reference 6

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This paper cites I., Mwanza, J.-C., Warren, J.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma I., Mwanza, J.-C., Warren, J

Reference 7

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This paper cites D., Morgan, W.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma D., Morgan, W

Reference 8

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This paper cites Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion).

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion)

Reference 9

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This paper cites M., Kucukelbir, A., McAuliffe, J.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma M., Kucukelbir, A., McAuliffe, J

Reference 10

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This paper cites R., Holan, S.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma R., Holan, S

Reference 11

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This paper cites R., Eilers, P.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma R., Eilers, P

Reference 12

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This paper cites P., Gelfand, A.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma P., Gelfand, A

Reference 13

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This paper cites Infogan: Interpretable representation learning by information maximizing generative adversarial nets.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Infogan: Interpretable representation learning by information maximizing generative adversarial nets

Reference 14

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This paper cites an unresolved cited work.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Unresolved cited work

Reference 15

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This paper cites C., Bengio, Y., 2015.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma C., Bengio, Y., 2015

Reference 16

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This paper cites Fixed rank kriging for very large spatial data sets.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Fixed rank kriging for very large spatial data sets

Reference 17

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This paper cites O., Gelfand, A.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma O., Gelfand, A

Reference 18

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This paper cites P., Laird, N.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma P., Laird, N

Reference 19

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Observation b038e30c-ae16-4911-b6ce-24bfe75e86ac · outbound

This paper cites Tutorial on Variational Autoencoders.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Tutorial on Variational Autoencoders

Reference 20

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This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Adaptive subgradient methods for online learning and stochastic optimization

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.510859Z

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This paper cites W., Hitchings, R.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma W., Hitchings, R

Reference 22

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This paper cites Approximate likelihood for large irregularly spaced spatial data.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Approximate likelihood for large irregularly spaced spatial data

Reference 23

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This paper cites Generative adversarial nets.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Generative adversarial nets

Reference 24

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This paper cites M., Shapiro, L.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma M., Shapiro, L

Reference 25

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Observation d5f8a944-0321-45a9-b1e7-41e25f3504f3 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Adam: A Method for Stochastic Optimization

Reference 26

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This paper cites Auto-Encoding Variational Bayes.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Auto-Encoding Variational Bayes

Reference 27

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This paper cites E., 2012.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma E., 2012

Reference 28

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This paper cites A., 1951.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma A., 1951

Reference 29

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This paper cites M., Qi, J., 2000.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma M., Qi, J., 2000

Reference 30

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This paper cites Deep learning.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Deep learning

Reference 31

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This paper cites Y., 2009.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Y., 2009

Reference 32

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Observation 80ab3d2c-8eb8-42c3-ba81-25230ddb9f90 · outbound

This paper cites G., Lei, X., Breslow, N., 2000.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma G., Lei, X., Breslow, N., 2000

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.392870Z

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This paper cites Z., Maresh, E.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Z., Maresh, E

Reference 34

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This paper cites S., Hobbs, B.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma S., Hobbs, B

Reference 35

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This paper cites G., Wang, Y., Zhu, H., Initiative, A.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma G., Wang, Y., Zhu, H., Initiative, A

Reference 36

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This paper cites L., Alfaro-Almagro, F., Bangerter, N.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma L., Alfaro-Almagro, F., Bangerter, N

Reference 37

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This paper cites S., Kong, L., Zhu, H., Initiative, A.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma S., Kong, L., Zhu, H., Initiative, A

Reference 38

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This paper cites E., Ormerod, J.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma E., Ormerod, J

Reference 39

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This paper cites D., Trujillo-Barreto, N.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma D., Trujillo-Barreto, N

Reference 40

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This paper cites G., Sokolov, V., et al., 2017.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma G., Sokolov, V., et al., 2017

Reference 41

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Observation f04294a8-a716-42ab-a8e1-06d88b539c91 · outbound

This paper cites Identification and correction of spurious spatial correlations in microarray data.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Identification and correction of spurious spatial correlations in microarray data

Reference 42

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Observation 479d88b1-9f9c-40b3-9a80-5d80cc00d6bc · outbound

This paper cites Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

Reference 43

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This paper cites Stochastic Backpropagation and Approximate Inference in Deep Generative Models.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Stochastic Backpropagation and Approximate Inference in Deep Generative Models

Reference 44

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This paper cites Distribution Matching in Variational Inference.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Distribution Matching in Variational Inference

Reference 45

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This paper cites RStudio: Integrated Development Environment for R.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma RStudio: Integrated Development Environment for R

Reference 46

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This paper cites E., Hinton, G.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma E., Hinton, G

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.268189Z

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Observation 564148c6-544e-4de1-a57f-b11505e133f7 · outbound

This paper cites A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in greater london.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma A spatio-temporal model for estimating the long-term effects of air pollution on respiratory hospital admissions in greater london

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.257670Z

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This paper cites Learning deep generative models.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Learning deep generative models

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.246930Z

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Observation c9266da3-615a-4586-aeac-eb9e19284d98 · outbound

This paper cites A., Boden, C., Zhang, Z., Pascual, J., Lee, T.-W., Zangwill, L.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma A., Boden, C., Zhang, Z., Pascual, J., Lee, T.-W., Zangwill, L

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.235881Z

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This paper cites A., 2012.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma A., 2012

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.225458Z

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Observation 68e33d3b-dbb8-4a39-b838-658b47909610 · outbound

This paper cites S., 1994.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma S., 1994

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.214963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a4d47b25-0151-4a83-9dd0-c5a320de11c8 · outbound

This paper cites Tapered covariance: Bayesian estimation and asymptotics.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Tapered covariance: Bayesian estimation and asymptotics

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.204751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 63483c35-42d0-4906-8f94-e226f5488a40 · outbound

This paper cites M., Nichols, T.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma M., Nichols, T

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.192783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7cf08dee-de05-4310-8665-808ccaa958a2 · outbound

This paper cites L., 2012.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma L., 2012

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.181686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 123ffea5-21eb-43dc-ab3a-9ec9cac89dfa · outbound

This paper cites Y., Quigley, H.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Y., Quigley, H

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.170465Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 51effcf4-a6fb-4995-b1f2-4fcc5e322b67 · outbound

This paper cites E., Keeney, S., 2012.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma E., Keeney, S., 2012

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.159137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 17e889ad-bd0b-49c0-8609-f5c500e8ce22 · outbound

This paper cites C., Smith, S.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma C., Smith, S

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.148248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 45c1474f-5c4c-43d0-b3b6-23bc178c67f0 · outbound

This paper cites V., 1988.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma V., 1988

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.137857Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 736c558a-dcea-47ac-97c5-b05e32c5aa83 · outbound

This paper cites A bayesian approach for estimating dynamic functional network connectivity in fmri data.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma A bayesian approach for estimating dynamic functional network connectivity in fmri data

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.126983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 07c34e2a-c8aa-4bec-9ca6-fe579a5e9e3b · outbound

This paper cites N., Aung, T., Medeiros, F.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma N., Aung, T., Medeiros, F

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.116041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1ea795b8-f563-4786-bad2-f447141ec6ba · outbound

This paper cites N., Leung, C.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma N., Leung, C

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.105017Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ebe726e6-aecd-44d0-a80b-91f1750b8408 · outbound

This paper cites C., Lee, C.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma C., Lee, C

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.093169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5af44ebd-1ee0-45af-96e4-869d67cb78f3 · outbound

This paper cites K., Cressie, N., 1999.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma K., Cressie, N., 1999

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.081994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 10bc24a2-5dc9-46d9-8940-5be2bf38a3df · outbound

This paper cites W., Jenkinson, M., Brady, J.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma W., Jenkinson, M., Brady, J

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.070719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e85e5b8b-1283-428c-b458-1c7bb6e96d9c · outbound

This paper cites Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.057598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e82c0203-50da-412b-a687-8f559e2fb29e · outbound

This paper cites J., Daga, F.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma J., Daga, F

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.043147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 6db29974-85c0-4f91-8d18-514dc164836d · outbound

This paper cites Generative Adversarial Network in Medical Imaging: A Review.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Generative Adversarial Network in Medical Imaging: A Review

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-14T11:24:38.802007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8a1258e-ddfc-468f-8c4b-01259550da55 · outbound

This paper cites Detection of longitudinal visual field progression in glaucoma using machine learning.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Detection of longitudinal visual field progression in glaucoma using machine learning

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.029715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 28f52882-a552-483f-b7ec-e2f66d26ac73 · outbound

This paper cites A., Majewski, T., Czerniak, B.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma A., Majewski, T., Czerniak, B

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.016010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e9a82267-9152-40df-a9b1-aa73378faf13 · outbound

This paper cites M., Vannucci, M., et al., 2016 b.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma M., Vannucci, M., et al., 2016 b

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:39.003453Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 9829fd4d-cc65-4711-b886-84ad0d4a2da2 · outbound

This paper cites InfoVAE: Information Maximizing Variational Autoencoders.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma InfoVAE: Information Maximizing Variational Autoencoders

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-14T11:24:38.817815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4f02798-b422-468a-a74d-057fe8e3a262 · outbound

This paper cites Towards Deeper Understanding of Variational Autoencoding Models.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma Towards Deeper Understanding of Variational Autoencoding Models

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-14T11:24:38.821765Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e060356a-265d-45a6-8ebe-0cc53692d3ab · outbound

This paper cites A., Saunders, L.

Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma A., Saunders, L

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T11:24:38.990593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

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