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[1] Thomas Serre,et al. Learning long-range spatial dependencies with horizontal gated-recurrent units , 2018, NeurIPS.
[2] S. Basu,et al. Analysis of Spatial Autocorrelation in House Prices , 1998 .
[3] Yang Zhang,et al. Point Cloud GAN , 2018, DGS@ICLR.
[4] Philip C. Treleaven,et al. Generative adversarial networks for financial trading strategies fine-tuning and combination , 2019, Quantitative Finance.
[5] R. Pace,et al. Sparse spatial autoregressions , 1997 .
[6] Simon Osindero,et al. Conditional Generative Adversarial Nets , 2014, ArXiv.
[7] Karsten Müller,et al. Soccer Jersey Number Recognition Using Convolutional Neural Networks , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).
[8] W. F. Krajewski,et al. Spatial rainfall estimation by linear and non-linear co-kriging of radar-rainfall and raingage data , 1989 .
[9] Raymond Y. K. Lau,et al. Least Squares Generative Adversarial Networks , 2016, 2017 IEEE International Conference on Computer Vision (ICCV).
[10] W. Tobler. A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .
[11] Hayit Greenspan,et al. Synthetic data augmentation using GAN for improved liver lesion classification , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).
[12] Francisco Martínez-Álvarez,et al. A novel ensemble modeling approach for the spatial prediction of tropical forest fire susceptibility using LogitBoost machine learning classifier and multi-source geospatial data , 2018, Theoretical and Applied Climatology.
[13] Yang Wang,et al. MARTA GANs: Unsupervised Representation Learning for Remote Sensing Image Classification , 2016, IEEE Geoscience and Remote Sensing Letters.
[14] Stefano Ermon,et al. Tile2Vec: Unsupervised representation learning for spatially distributed data , 2018, AAAI.
[15] Geoff S. Nitschke,et al. Improving Deep Learning with Generic Data Augmentation , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).
[16] P. Moran. Notes on continuous stochastic phenomena. , 1950, Biometrika.
[17] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[18] Jukka Heikkonen,et al. Estimating the prediction performance of spatial models via spatial k-fold cross validation , 2017, Int. J. Geogr. Inf. Sci..
[19] Andrew Gordon Wilson,et al. GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration , 2018, NeurIPS.
[20] Yin Zhou,et al. VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[21] Xin Yao,et al. Evolutionary Generative Adversarial Networks , 2018, IEEE Transactions on Evolutionary Computation.
[22] Joan Bruna,et al. Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.
[23] Joachim Denzler,et al. Deep learning and process understanding for data-driven Earth system science , 2019, Nature.
[24] Sudipto Banerjee,et al. Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets , 2014, Journal of the American Statistical Association.
[25] Kilian Q. Weinberger,et al. Snapshot Ensembles: Train 1, get M for free , 2017, ICLR.
[26] Dean C. Barratt,et al. Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks , 2017, CMMI/RAMBO/SWITCH@MICCAI.
[27] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[28] Minh N. Do,et al. Fast Guided Global Interpolation for Depth and Motion , 2016, ECCV.
[29] Ryan P. Adams,et al. Factorized Point Process Intensities: A Spatial Analysis of Professional Basketball , 2014, ICML.
[30] Alexander Brenning,et al. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.
[31] Hao Su,et al. A Point Set Generation Network for 3D Object Reconstruction from a Single Image , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[32] Luc Anselin,et al. A Local Indicator of Multivariate Spatial Association: Extending Geary's c , 2018, Geographical Analysis.
[33] Yan Li,et al. Spatial Ensemble Learning for Heterogeneous Geographic Data with Class Ambiguity , 2019, ACM Trans. Intell. Syst. Technol..
[34] Yiorgos Makris,et al. Handling discontinuous effects in modeling spatial correlation of wafer-level analog/RF tests , 2013, 2013 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[35] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[36] L. Anselin. Local Indicators of Spatial Association—LISA , 2010 .
[37] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[38] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[39] Trevor Hastie,et al. The Elements of Statistical Learning , 2001 .
[40] Wojciech Zaremba,et al. Improved Techniques for Training GANs , 2016, NIPS.
[41] Liang Chen,et al. GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks , 2018, ArXiv.
[42] Jerome P. Reiter,et al. Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography , 2014, 1407.7795.
[43] Wei Wei,et al. COCO-GAN: Generation by Parts via Conditional Coordinating , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[44] Mark J. van der Laan,et al. Optimal Spatial Prediction Using Ensemble Machine Learning , 2016, The international journal of biostatistics.
[45] Le Song,et al. Wasserstein Learning of Deep Generative Point Process Models , 2017, NIPS.
[46] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[47] Eric R. Ziegel,et al. The Elements of Statistical Learning , 2003, Technometrics.
[48] Yu Liu,et al. Spatial interpolation using conditional generative adversarial neural networks , 2019, Int. J. Geogr. Inf. Sci..