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Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise.
The three-body problem
Valtonen MJ, Mauri Valtonen, and Hannu Karttunen · 2006
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Strictly proper scoring rules, prediction, and estimation
Tilmann Gneiting and Adrian E Raftery · 2007
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Improving neural networks by preventing co-adaptation of feature detectors
Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov · 2012
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Conditional image generation with pixelcnn decoders
Aaron Van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Natural-parameter networks: A class of probabilistic neural networks
Hao Wang, SHI Xingjian, and Dit-Yan Yeung · 2016
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Towards bayesian deep learning: A framework and some existing methods
Hao Wang and Dit-Yan Yeung · 2016
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Tim Salimans, Andrej Karpathy, Xi Chen, and Diederik P Kingma · 2017
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Deep learning for precipitation nowcasting: A benchmark and a new model
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, L · 2017
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Fr \ \backslash ’echet audio distance: A metric for evaluating music enhancement algorithms
Kevin Kilgour, Mauricio Zuluaga, Dominik Roblek, and Matthew Sharifi · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Kristina Preuer, Philipp Renz, Thomas Unterthiner, Sepp Hochreiter, and Gunter Klambauer · 2018
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Eidetic 3D LSTM: A model for video prediction and beyond
Yunbo Wang, Lu Jiang, Ming-Hsuan Yang, Li-Jia Li, Mingsheng Long, and Li Fei-Fei · 2018
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Group normalization
Yuxin Wu and Kaiming He · 2018
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Fvd: A new metric for video generation
Thomas Unterthiner, Sjoerd van Steenkiste, Karol Kurach, Raphaël Marinier, Marcin Michalski, and Sylvain Gelly · 2019
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Scaling autoregressive video models
Dirk Weissenborn, Oscar Täckström, and Jakob Uszkoreit · 2019
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Disentangling physical dynamics from unknown factors for unsupervised video prediction
Vincent Le Guen and Nicolas Thome · 2020
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The era5 global reanalysis
Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, et al · 2020
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Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Ruslan Rakhimov, Denis Volkhonskiy, Alexey Artemov, Denis Zorin, and Evgeny Burnaev · 2020
Training-free uncertainty estimation for neural networks
Lu Mi, Hao Wang, Yonglong Tian, and Nir Shavit · 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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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
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Metnet: A neural weather model for precipitation forecasting
Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver, Tim Salimans, Shreya Agrawal, Jason Hickey, and Nal Kalchbrenner · 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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A survey on bayesian deep learning
Hao Wang and Dit-Yan Yeung · 2020
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Diffusion models beat gans on image synthesis
Prafulla Dhariwal and Alexander Nichol · 2021
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Skillful twelve hour precipitation forecasts using large context neural networks
Lasse Espeholt, Shreya Agrawal, Casper Sønderby, Manoj Kumar, Jonathan Heek, Carla Bromberg, Cenk Gazen, Jason Hickey, Aaron Bell, and Nal Kalchbrenner · 2021
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Taming transformers for high-resolution image synthesis
Patrick Esser, Robin Rombach, and Bjorn Ommer · 2021
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Chitwan Saharia, William Chan, Saurabh Saxena, Lala Li, Jay Whang, Emily Denton, Seyed Kamyar Seyed Ghasemipour, Burcu Karagol Ayan, S Sara Mahdavi, Rapha Gontijo Lopes, et al · 2022
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Masked conditional video diffusion for prediction, generation, and interpolation
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PredRNN: A recurrent neural network for spatiotemporal predictive learning
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Physdiff: Physics-guided human motion diffusion model
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Accurate medium-range global weather forecasting with 3d neural networks
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Learning physical models that can respect conservation laws
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Diffusion models for high-resolution solar forecasts
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Conditional image-to-video generation with latent flow diffusion models, 2023
Haomiao Ni, Changhao Shi, Kai Li, Sharon X. Huang, and Martin Renqiang Min · 2023
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Guiding continuous operator learning through physics-based boundary constraints
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Variational imbalanced regression: Fair uncertainty quantification via probabilistic smoothing
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Video probabilistic diffusion models in projected latent space
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Adding conditional control to text-to-image diffusion models
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A self-attention–based neural network for three-dimensional multivariate modeling and its skillful enso predictions
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