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Transformers are widely used deep learning architectures.
Remote sensing, ecological water quality modelling and in situ measurements: a case study in shallow lakes
AG Dekker, Ž Zamurović-Nenad, HJ Hoogenboom, and SWM Peters · 1996
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Hierarchical modeling and analysis for spatial data
Sudipto Banerjee, Bradley P Carlin, and Alan E Gelfand · 2003
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Comparison of spatial interpolation methods for the estimation of air quality data
David W Wong, Lester Yuan, and Susan A Perlin · 2004
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A review of spatial interpolation methods for environmental scientists
Jin Li and Andrew D Heap · 2008
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Modeling linkages between sediment resuspension and water quality in a shallow, eutrophic, wind-exposed lake
Eu Gene Chung, Fabián A Bombardelli, and S Geoffrey Schladow · 2009
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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High performance computing and numerical modelling, 2014
Volker Springel · 2014
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Hierarchical modeling and analysis for spatial data
Sudipto Banerjee, Bradley P Carlin, and Alan E Gelfand · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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John Bradshaw, Alexander G de G Matthews, and Zoubin Ghahramani · 2017
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Image transformer
Niki Parmar, Ashish Vaswani, Jakob Uszkoreit, Lukasz Kaiser, Noam Shazeer, Alexander Ku, and Dustin Tran · 2018
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Accurate uncertainties for deep learning using calibrated regression
Volodymyr Kuleshov, Nathan Fenner, and Stefano Ermon · 2018
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Graph transformer networks
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim · 2019
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The role of algae in fine sediment flocculation: In-situ and laboratory measurements
Zhirui Deng, Qing He, Zeinab Safar, and Claire Chassagne · 2019
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Transformer-xl: Attentive language models beyond a fixed-length context
Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V Le, and Ruslan Salakhutdinov · 2019
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Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
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Axial attention in multidimensional transformers
Jonathan Ho, Nal Kalchbrenner, Dirk Weissenborn, and Tim Salimans · 2019
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Deep learning for physical processes: Incorporating prior scientific knowledge
Emmanuel De Bézenac, Arthur Pajot, and Patrick Gallinari · 2019
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Neural operator: Graph kernel network for partial differential equations
Anima Anandkumar, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Nikola Kovachki, Zongyi Li, Burigede Liu, and Andrew Stuart · 2020
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Longformer: The long-document transformer
Iz Beltagy, Matthew E Peters, and Arman Cohan · 2020
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Reformer: The efficient transformer
Nikita Kitaev, Łukasz Kaiser, and Anselm Levskaya · 2020
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Multipole graph neural operator for parametric partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Andrew Stuart, Kaushik Bhattacharya, and Anima Anandkumar · 2020
Cited alongside, same era.
Fourier neural operator for parametric partial differential equations
Zongyi Li, Nikola Kovachki, Kamyar Azizzadenesheli, Burigede Liu, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar · 2020
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Multimodal deep learning based crop classification using multispectral and multitemporal satellite imagery
Krishna Karthik Gadiraju, Bharathkumar Ramachandra, Zexi Chen, and Ranga Raju Vatsavai · 2020
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Climate downscaling using ynet: A deep convolutional network with skip connections and fusion
Yumin Liu, Auroop R Ganguly, and Jennifer Dy · 2020
Cited alongside, same era.
Curvanet: Geometric deep learning based on directional curvature for 3d shape analysis
Wenchong He, Zhe Jiang, Chengming Zhang, and Arpan Man Sainju · 2020
Next point-of-interest recommendation with auto-correlation enhanced multi-modal transformer network
Yanjun Qin, Yuchen Fang, Haiyong Luo, Fang Zhao, and Chenxing Wang · 2022
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Quadtree attention for vision transformers
Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan · 2022
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Sufficient vision transformer
Zhi Cheng, Xiu Su, Xueyu Wang, Shan You, and Chang Xu · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Qitian Wu, Wentao Zhao, Zenan Li, David P Wipf, and Junchi Yan · 2022
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Nagphormer: A tokenized graph transformer for node classification in large graphs
Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He · 2022
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Cited alongside, same era.
Traffic flow prediction via spatial temporal graph neural network
Xiaoyang Wang, Yao Ma, Yiqi Wang, Wei Jin, Xin Wang, Jiliang Tang, Caiyan Jia, and Jian Yu · 2020
Cited alongside, same era.
Hyperparameter ensembles for robustness and uncertainty quantification
Florian Wenzel, Jasper Snoek, Dustin Tran, and Rodolphe Jenatton · 2020
Cited alongside, same era.
Improving model calibration with accuracy versus uncertainty optimization
Ranganath Krishnan and Omesh Tickoo · 2020
Cited alongside, same era.
Perceiver: General perception with iterative attention
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman, and Joao Carreira · 2021
Cited alongside, same era.
Dynamicvit: Efficient vision transformers with dynamic token sparsification
Yongming Rao, Wenliang Zhao, Benlin Liu, Jiwen Lu, Jie Zhou, and Cho-Jui Hsieh · 2021
Cited alongside, same era.
Rethinking graph transformers with spectral attention
Devin Kreuzer, Dominique Beaini, Will Hamilton, Vincent Létourneau, and Prudencio Tossou · 2021
Cited alongside, same era.
Adaptive fourier neural operators: Efficient token mixers for transformers
John Guibas, Morteza Mardani, Zongyi Li, Andrew Tao, Anima Anandkumar, and Bryan Catanzaro · 2021
Cited alongside, same era.
Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee · 2022
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Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al · 2022
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Neural implicit flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data, 2022
Shaowu Pan, Steven L Brunton, and J Nathan Kutz · 2022
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Continuous pde dynamics forecasting with implicit neural representations
Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, and Patrick Gallinari · 2022
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Crom: Continuous reduced-order modeling of pdes using implicit neural representations
Peter Yichen Chen, Jinxu Xiang, Dong Heon Cho, Yue Chang, GA Pershing, Henrique Teles Maia, Maurizio Chiaramonte, Kevin Carlberg, and Eitan Grinspun · 2022
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Gnn-surrogate: A hierarchical and adaptive graph neural network for parameter space exploration of unstructured-mesh ocean simulations
Neng Shi, Jiayi Xu, Skylar W Wurster, Hanqi Guo, Jonathan Woodring, Luke P Van Roekel, and Han-Wei Shen · 2022
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Dual-graph learning convolutional networks for interpretable alzheimer’s disease diagnosis
Tingsong Xiao, Lu Zeng, Xiaoshuang Shi, Xiaofeng Zhu, and Guorong Wu · 2022
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Earth imagery segmentation on terrain surface with limited training labels: A semi-supervised approach based on physics-guided graph co-training
Wenchong He, Arpan Man Sainju, Zhe Jiang, Da Yan, and Yang Zhou · 2022
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Quadtree attention for vision transformers
Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan · 2022
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Quantifying and reducing registration uncertainty of spatial vector labels on earth imagery
Wenchong He, Zhe Jiang, Marcus Kriby, Yiqun Xie, Xiaowei Jia, Da Yan, and Yang Zhou · 2022
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Climax: A foundation model for weather and climate
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover · 2023
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Point-particle drag, lift, and torque closure models using machine learning: Hierarchical approach and interpretability
B Siddani and S Balachandar · 2023
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Difformer: Scalable (graph) transformers induced by energy constrained diffusion
Qitian Wu, Chenxiao Yang, Wentao Zhao, Yixuan He, David Wipf, and Junchi Yan · 2023
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Neural operator: Learning maps between function spaces with applications to pdes
Nikola B Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew M Stuart, and Anima Anandkumar · 2023
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Accurate medium-range global weather forecasting with 3d neural networks
Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, and Qi Tian · 2023
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Climax: A foundation model for weather and climate, january 2023
Tung Nguyen, Johannes Brandstetter, Ashish Kapoor, Jayesh K Gupta, and Aditya Grover · 2023
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Geometry-informed neural operator for large-scale 3d pdes
Zongyi Li, Nikola Borislavov Kovachki, Chris Choy, Boyi Li, Jean Kossaifi, Shourya Prakash Otta, Mohammad Amin Nabian, Maximilian Stadler, Christian Hundt, Kamyar Azizzadenesheli, et al · 2023
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Wenchong He and Zhe Jiang · 2023
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