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Spatio-temporal (ST) prediction has garnered a De facto attention in earth sciences, such as meteorological prediction, human mobility perception.
Navier-stokes equations
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Recognizing human actions: a local svm approach
Christian Schuldt, Ivan Laptev, and Barbara Caputo · 2004
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A linear non-gaussian acyclic model for causal discovery
Shohei Shimizu, Patrik O Hoyer, Aapo Hyvärinen, Antti Kerminen, and Michael Jordan · 2006
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Causality
Judea Pearl · 2009
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Multiphysics modeling using COMSOL®: a first principles approach
Roger W Pryor · 2009
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Denoising diffusion implicit models
Jiaming Song, Chenlin Meng, and Stefano Ermon · 2010
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Discovering spatio-temporal causal interactions in traffic data streams
Wei Liu, Yu Zheng, Sanjay Chawla, Jing Yuan, and Xie Xing · 2011
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2011
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Causal discovery from spatio-temporal data with applications to climate science
Imme Ebert-Uphoff and Yi Deng · 2014
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2015
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Action-conditional video prediction using deep networks in atari games
Junhyuk Oh, Xiaoxiao Guo, Honglak Lee, Richard L Lewis, and Satinder Singh · 2015
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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit-Yan Yeung, Wai-Kin Wong, and Wang-chun Woo · 2015
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Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
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Context encoders: Feature learning by inpainting
Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Grad-cam: Why did you say that?
Ramprasaath R Selvaraju, Abhishek Das, Ramakrishna Vedantam, Michael Cogswell, Devi Parikh, and Dhruv Batra · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 2017
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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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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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The book of why: the new science of cause and effect
Judea Pearl and Dana Mackenzie · 2018
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Mocogan: Decomposing motion and content for video generation
Sergey Tulyakov, Ming-Yu Liu, Xiaodong Yang, and Jan Kautz · 2018
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Hierarchical long-term video prediction without supervision
Ruben Villegas, Dumitru Erhan, Honglak Lee, et al · 2018
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Dags with no tears: Continuous optimization for structure learning
Xun Zheng, Bryon Aragam, Pradeep K Ravikumar, and Eric P Xing · 2018
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Pluralistic image completion
Chuanxia Zheng, Tat-Jen Cham, and Jianfei Cai · 2019
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Dominant patterns of interaction between the tropics and mid-latitudes in boreal summer: Causal relationships and the role of time-scales
Giorgia Di Capua, Jakob Runge, Reik V Donner, Bart van den Hurk, Andrew G Turner, Ramesh Vellore, Raghavan Krishnan, and Dim Coumou · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
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In search of lost domain generalization
Ishaan Gulrajani and David Lopez-Paz · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
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Causal networks for climate model evaluation and constrained projections
Peer Nowack, Jakob Runge, Veronika Eyring, and Joanna D Haigh · 2020
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Sevir: A storm event imagery dataset for deep learning applications in radar and satellite meteorology
Mark Veillette, Siddharth Samsi, and Chris Mattioli · 2020
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Uctgan: Diverse image inpainting based on unsupervised cross-space translation
Cape: Channel-attention-based pde parameter embeddings for sciml
Makoto Takamoto, Francesco Alesiani, and Mathias Niepert · 2022
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A spatial temporal graph neural network model for predicting flashover in arbitrary building floorplans
Wai Cheong Tam, Eugene Yujun Fu, Jiajia Li, Xinyan Huang, Jian Chen, and Michael Xuelin Huang · 2022
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A spatiotemporal stochastic climate model for benchmarking causal discovery methods for teleconnections
Xavier-Andoni Tibau, Christian Reimers, Andreas Gerhardus, Joachim Denzler, Veronika Eyring, and Jakob Runge · 2022
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Sfl: A high-precision traffic flow predictor for supporting intelligent transportation systems
Zepu Wang, Peng Sun, Yulin Hu, and Azzedine Boukerche · 2022
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Transformers in time series: A survey
Qingsong Wen, Tian Zhou, Chaoli Zhang, Weiqi Chen, Ziqing Ma, Junchi Yan, and Liang Sun · 2022
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Lei Zhao, Qihang Mo, Sihuan Lin, Zhizhong Wang, Zhiwen Zuo, Haibo Chen, Wei Xing, and Dongming Lu · 2020
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Deep-learning approach to first-principles transport simulations
Marius Bürkle, Umesha Perera, Florian Gimbert, Hisao Nakamura, Masaaki Kawata, and Yoshihiro Asai · 2021
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Mau: A motion-aware unit for video prediction and beyond
Zheng Chang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Yan Ye, Xiang Xinguang, and Wen Gao · 2021
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Discovering state variables hidden in experimental data
Boyuan Chen, Kuang Huang, Sunand Raghupathi, Ishaan Chandratreya, Qiang Du, and Hod Lipson · 2021
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Big data algorithms and applications in intelligent transportation system: A review and bibliometric analysis
Sepideh Kaffash, An Truong Nguyen, and Joe Zhu · 2021
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Physics-informed machine learning
George Em Karniadakis, Ioannis G Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang · 2021
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Wilds: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, et al · 2021
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Deconfounding to explanation evaluation in graph neural networks
Ying-Xin Wu, Xiang Wang, An Zhang, Xia Hu, Fuli Feng, Xiangnan He, and Tat-Seng Chua · 2022
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Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al · 2023
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Rethinking urban mobility prediction: A super-multivariate time series forecasting approach
Jinguo Cheng, Ke Li, Yuxuan Liang, Lijun Sun, Junchi Yan, and Yuankai Wu · 2023
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Disentangled generative models for robust prediction of system dynamics
Stathi Fotiadis, Mario Lino Valencia, Shunlong Hu, Stef Garasto, Chris D Cantwell, and Anil Anthony Bharath · 2023
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Uncertainty-aware probabilistic graph neural networks for road-level traffic accident prediction
Xiaowei Gao, Xinke Jiang, Dingyi Zhuang, Huanfa Chen, Shenhao Wang, Stephen Law, and James Haworth · 2023
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Ziyu Guo, Renrui Zhang, Xiangyang Zhu, Yiwen Tang, Xianzheng Ma, Jiaming Han, Kexin Chen, Peng Gao, Xianzhi Li, Hongsheng Li, et al · 2023
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Uncertainty quantification via spatial-temporal tweedie model for zero-inflated and long-tail travel demand prediction
Xinke Jiang, Dingyi Zhuang, Xianghui Zhang, Hao Chen, Jiayuan Luo, and Xiaowei Gao · 2023
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Holodiffusion: Training a 3d diffusion model using 2d images
Animesh Karnewar, Andrea Vedaldi, David Novotny, and Niloy J Mitra · 2023
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Advancing pose-guided image synthesis with progressive conditional diffusion models
Fei Shen, Hu Ye, Jun Zhang, Cong Wang, Xiao Han, and Yang Wei · 2023
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Temporal attention unit: Towards efficient spatiotemporal predictive learning
Cheng Tan, Zhangyang Gao, Lirong Wu, Yongjie Xu, Jun Xia, Siyuan Li, and Stan Z Li · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Sst: A simplified swin transformer-based model for taxi destination prediction based on existing trajectory
Zepu Wang, Yifei Sun, Zhiyu Lei, Xincheng Zhu, and Peng Sun · 2023
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Mmvp: Motion-matrix-based video prediction
Yiqi Zhong, Luming Liang, Ilya Zharkov, and Ulrich Neumann · 2023
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Maintaining the status quo: Capturing invariant relations for ood spatiotemporal learning
Zhengyang Zhou, Qihe Huang, Kuo Yang, Kun Wang, Xu Wang, Yudong Zhang, Yuxuan Liang, and Yang Wang · 2023
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Nuwats: Mending every incomplete time series
Jinguo Cheng, Chunwei Yang, Wanlin Cai, Yuxuan Liang, and Yuankai Wu · 2024
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Cat-gnn: Enhancing credit card fraud detection via causal temporal graph neural networks
Yifan Duan, Guibin Zhang, Shilong Wang, Xiaojiang Peng, Wang Ziqi, Junyuan Mao, Hao Wu, Xinke Jiang, and Kun Wang · 2024
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Wildfirespreadts: A dataset of multi-modal time series for wildfire spread prediction
Sebastian Gerard, Yu Zhao, and Josephine Sullivan · 2024
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Incomplete graph learning via attribute-structure decoupled variational auto-encoder
Xinke Jiang, Zidi Qin, Jiarong Xu, and Xiang Ao · 2024
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Largest: A benchmark dataset for large-scale traffic forecasting
Xu Liu, Yutong Xia, Yuxuan Liang, Junfeng Hu, Yiwei Wang, Lei Bai, Chao Huang, Zhenguang Liu, Bryan Hooi, and Roger Zimmermann · 2024
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Wonder3d: Single image to 3d using cross-domain diffusion
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Timeseries suppliers allocation risk optimization via deep black litterman model
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The heterophilic snowflake hypothesis: Training and empowering gnns for heterophilic graphs
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Deciphering spatio-temporal graph forecasting: A causal lens and treatment
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Faima: Feature-aware in-context learning for multi-domain aspect-based sentiment analysis
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