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From ecology to atmospheric sciences, many academic disciplines deal with data characterized by intricate spatio-temporal complexities, the modeling of which often requires specialized approaches.
Augmenting correlation structures in spatial data using deep generative models
Klemmer, K.; Koshiyama, A.; and Flennerhag, S. 2019 · 1905
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Local Indicators of Spatial Association—LISA
Anselin, L. 1995 · 1995
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Spatiotemporal prediction for log-Gaussian Cox processes
Brix, A.; and Diggle, P. J. 2001 · 2001
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A space-time permutation scan statistic for disease outbreak detection
Kulldorff, M.; Heffernan, R.; Hartman, J.; Assunção, R.; and Mostashari, F. 2005 · 2005
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Integrating structured biological data by Kernel Maximum Mean Discrepancy
Borgwardt, K. M.; Gretton, A.; Rasch, M. J.; Kriegel, H. P.; Schölkopf, B.; and Smola, A. J. 2006 · 2006
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Local spatial heteroscedasticity (LOSH)
Ord, J. K.; and Getis, A. 2012 · 2012
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Sinkhorn distances: Lightspeed computation of optimal transport
Cuturi, M. 2013 · 2013
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Space-time correlations in turbulent flow: A review
Wallace, J. M. 2014 · 2014
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. L. 2015 · 2015
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Bayesian inference and data augmentation schemes for spatial, spatiotemporal and multivariate log-gaussian cox processes in R
Taylor, B. M.; Davies, T. M.; Rowlingson, B. S.; and Diggle, P. J. 2015 · 2015
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CNN-based patch matching for optical flow with thresholded hinge embedding loss
Bailer, C.; Varanasi, K.; and Stricker, D. 2017 · 2017
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Measuring Moran’s I in a cost-efficient manner to describe a land-cover change pattern in large-scale remote sensing imagery
Das, M.; and Ghosh, S. K. 2017 · 2017
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Extending Moran’s Index for Measuring Spatiotemporal Clustering of Geographic Events
Lee, J.; and Li, S. 2017 · 2017
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Revisiting classifier two-sample tests
Lopez-Paz, D.; and Oquab, M. 2019 · 2017
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ExtremeWeather: A large-scale climate dataset for semi-supervised detection, localization, and understanding of extreme weather events
Racah, E.; Beckham, C.; Maharaj, T.; Kahou, S. E.; Prabhat; and Pal, C. 2017 · 2017
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From ITDL to Place2Vec – Reasoning About Place Type Similarity and Relatedness by Learning Embeddings From Augmented Spatial Contexts
Yan, B.; Mai, G.; Janowicz, K.; and Gao, S. 2017 · 2017
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Learning Generative Models with Sinkhorn Divergences
Genevay, A.; Peyre, G.; and Cuturi, M. 2018 · 2018
Cited alongside, same era.
Testing for local structure in spatiotemporal point pattern data
Siino, M.; Rodríguez-Cortés, F. J.; Mateu, J.; and Adelfio, G. 2018 · 2018
Cited alongside, same era.
Mapping the Spatiotemporal Dynamics of Europe’s Land Surface Temperatures
Sismanidis, P.; Bechtel, B.; Keramitsoglou, I.; and Kiranoudis, C. T. 2018 · 2018
Cited alongside, same era.
MoCoGAN: Decomposing Motion and Content for Video Generation
Tulyakov, S.; Liu, M. Y.; Yang, X.; and Kautz, J. 2018 · 2018
Cited alongside, same era.
A statistical test on the local effects of spatially structured variance
Westerholt, R.; Resch, B.; Mocnik, F. B.; and Hoffmeister, D. 2018 · 2018
Cited alongside, same era.
Using Local Moran’s I to identify contamination hotspots of rare earth elements in urban soils of London
Deep Compositional Spatial Models
Zammit-Mangion, A.; Ng, T. L. J.; Vu, Q.; and Filippone, M. 2019 · 2019
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Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning
Zhang, Y.; Fu, Y.; Wang, P.; Li, X.; and Zheng, Y. 2019 · 2019
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COVID-GAN: Estimating Human Mobility Responses to COVID-19 Pandemic through Spatio-Temporal Conditional Generative Adversarial Networks
Bao, H.; Zhou, X.; Zhang, Y.; Li, Y.; and Xie, Y. 2020 · 2020
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Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss
Filntisis, P. P.; Efthymiou, N.; Potamianos, G.; and Maragos, P. 2020 · 2020
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Residual Correlation in Graph Neural Network Regression
Jia, J.; and Benson, A. R. 2020 · 2020
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Yuan, Y.; Cave, M.; and Zhang, C. 2018 · 2018
Cited alongside, same era.
Presence-only geographical priors for fine-grained image classification
Aodha, O. M.; Cole, E.; and Perona, P. 2019 · 2019
Cited alongside, same era.
Geo-Aware Networks for Fine-Grained Recognition
Chu, G.; Potetz, B.; Wang, W.; Howard, A.; Song, Y.; Brucher, F.; Leung, T.; Adam, H.; and Research, G. 2019 · 2019
Cited alongside, same era.
Measuring spatio-temporal autocorrelation in time series data of collective human mobility
Gao, Y.; Cheng, J.; Meng, H.; and Liu, Y. 2019 · 2019
Cited alongside, same era.
Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting
Geng, X.; Li, Y.; Wang, L.; Zhang, L.; Yang, Q.; Ye, J.; and Liu, Y. 2019 · 2019
Cited alongside, same era.
EL-GAN: Embedding loss driven generative adversarial networks for lane detection
Ghafoorian, M.; Nugteren, C.; Baka, N.; Booij, O.; and Hofmann, M. 2019 · 2019
Cited alongside, same era.
Quantifying Spatio-Temporal Characteristics via Moran’s Statistics
Matthews, J. L.; Diawara, N.; and Waller, L. A. 2019 · 2019
Cited alongside, same era.
DPM: A Novel Training Method for Physics-Informed Neural Networks in Extrapolation
Kim, J.; Lee, K.; Lee, D.; Jin, S. Y.; and Park, N. 2020 · 2020
Later among the works it cites.
JSI-GAN: GAN-based joint super-resolution and inverse tone-mapping with pixel-wise task-specific filters for UHD HDR video
Kim, S. Y.; Oh, J.; and Kim, M. 2020 · 2020
Later among the works it cites.
Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells
Mai, G.; Janowicz, K.; Yan, B.; Zhu, R.; Cai, L.; and Lao, N. 2020 · 2020
Later among the works it cites.
Physics-informed deep learning for incompressible laminar flows
Rao, C.; Sun, H.; and Liu, Y. 2020 · 2020
Later among the works it cites.
COT-GAN: Generating sequential data via causal optimal transport
Xu, T.; Wenliang, L. K.; Munn, M.; and Acciaio, B. 2020 · 2020
Later among the works it cites.
Curb-GAN: Conditional Urban Traffic Estimation through Spatio-Temporal Generative Adversarial Networks
Zhang, Y.; Li, Y.; Zhou, X.; Kong, X.; and Luo, J. 2020 · 2020
Later among the works it cites.
STI-GAN: Multimodal Pedestrian Trajectory Prediction Using Spatiotemporal Interactions and a Generative Adversarial Network
Huang, L.; Zhuang, J.; Cheng, X.; Xu, R.; and Ma, H. 2021 · 2021
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Auxiliary-task learning for geographic data with autoregressive embeddings
Klemmer, K.; and Neill, D. B. 2021 · 2021
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Generative modeling of spatio-temporal weather patterns with extreme event conditioning
Klemmer, K.; Saha, S.; Kahl, M.; Xu, T.; and Zhu, X. X. 2021 · 2021
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Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements
Zhang, J.; and Zhao, X. 2021 · 2021
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