{"list":"other","total":64,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"id":"2103.00020","title":"Learning Transferable Visual Models From Natural Language Supervision","created":"2021-02-26","cited_by_count":33744},{"id":"2005.14165","title":"Language Models are Few-Shot Learners","created":"2020-05-28","cited_by_count":32419},{"id":"2010.11929","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","created":"2020-10-22","cited_by_count":30186},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"2103.14030","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","created":"2021-03-25","cited_by_count":12139},{"id":"2002.05709","title":"A Simple Framework for Contrastive Learning of Visual Representations","created":"2020-02-13","cited_by_count":12049},{"id":"1807.03748","title":"Representation Learning with Contrastive Predictive Coding","created":"2018-07-10","cited_by_count":7777},{"id":"1911.05722","title":"Momentum Contrast for Unsupervised Visual Representation Learning","created":"2019-11-13","cited_by_count":7513},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"2108.07258","title":"On the Opportunities and Risks of Foundation Models","created":"2021-08-16","cited_by_count":4033},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"1910.03193","title":"DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators","created":"2019-10-08","cited_by_count":2018},{"id":"2007.14062","title":"Big Bird: Transformers for Longer Sequences","created":"2020-07-28","cited_by_count":1684},{"id":"2203.12602","title":"VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training","created":"2022-03-23","cited_by_count":1322},{"id":"2106.13230","title":"Video Swin Transformer","created":"2021-06-24","cited_by_count":1091},{"id":"2301.08243","title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","created":"2023-01-19","cited_by_count":1039},{"id":"2002.09405","title":"Learning to Simulate Complex Physics with Graph Networks","created":"2020-02-21","cited_by_count":880},{"id":"2010.03409","title":"Learning Mesh-Based Simulation with Graph Networks","created":"2020-10-07","cited_by_count":868},{"id":"1906.00121","title":"Graph WaveNet for Deep Spatial-Temporal Graph Modeling","created":"2019-05-31","cited_by_count":829},{"id":"1806.08804","title":"Hierarchical Graph Representation Learning with Differentiable Pooling","created":"2018-06-22","cited_by_count":787},{"id":"2202.11214","title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","created":"2022-02-22","cited_by_count":776},{"id":"1705.03122","title":"Convolutional Sequence to Sequence Learning","created":"2017-05-08","cited_by_count":552},{"id":"2301.10343","title":"ClimaX: A foundation model for weather and climate","created":"2023-01-24","cited_by_count":337},{"id":"2104.00954","title":"Skillful Precipitation Nowcasting using Deep Generative Models of Radar","created":"2021-04-02","cited_by_count":311},{"id":"2004.06100","title":"Pretrained Transformers Improve Out-of-Distribution Robustness","created":"2020-04-13","cited_by_count":294},{"id":"2212.12794","title":"GraphCast: Learning skillful medium-range global weather forecasting","created":"2022-12-24","cited_by_count":267},{"id":"1906.05433","title":"Tackling Climate Change with Machine Learning","created":"2019-06-10","cited_by_count":265},{"id":"2306.03838","title":"Spherical Fourier Neural Operators: Learning Stable Dynamics on the Sphere","created":"2023-06-06","cited_by_count":233},{"id":"2308.05732","title":"PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers","created":"2023-08-10","cited_by_count":216},{"id":"2302.01178","title":"Convolutional Neural Operators for robust and accurate learning of PDEs","created":"2023-02-02","cited_by_count":214},{"id":"2208.05419","title":"FourCastNet: Accelerating Global High-Resolution Weather Forecasting using Adaptive Fourier Neural Operators","created":"2022-08-08","cited_by_count":188},{"id":"1806.04731","title":"Deep learning to represent sub-grid processes in climate models","created":"2018-06-12","cited_by_count":156},{"id":"2003.12140","title":"MetNet: A Neural Weather Model for Precipitation Forecasting","created":"2020-03-24","cited_by_count":131},{"id":"2306.01984","title":"DYffusion: A Dynamics-informed Diffusion Model for Spatiotemporal Forecasting","created":"2023-06-03","cited_by_count":109},{"id":"2003.11927","title":"Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere","created":"2020-03-15","cited_by_count":96},{"id":"1902.09113","title":"Star-Transformer","created":"2019-02-25","cited_by_count":94},{"id":"2106.07617","title":"Delving Deep into the Generalization of Vision Transformers under Distribution Shifts","created":"2021-06-14","cited_by_count":85},{"id":"2306.06079","title":"Deep Learning for Day Forecasts from Sparse Observations","created":"2023-06-06","cited_by_count":72},{"id":"1912.12132","title":"Machine Learning for Precipitation Nowcasting from Radar Images","created":"2019-12-11","cited_by_count":56},{"id":"2009.04318","title":"Combining data assimilation and machine learning to infer unresolved scale parametrisation","created":"2020-09-09","cited_by_count":40},{"id":"2308.13280","title":"AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning","created":"2023-08-25","cited_by_count":40},{"id":"2302.06594","title":"Geometric Clifford Algebra Networks","created":"2023-02-13","cited_by_count":38},{"id":"2306.03110","title":"SwinRDM: Integrate SwinRNN with Diffusion Model towards High-Resolution and High-Quality Weather Forecasting","created":"2023-06-05","cited_by_count":32},{"id":"2307.05432","title":"Self-Supervised Learning with Lie Symmetries for Partial Differential Equations","created":"2023-07-11","cited_by_count":29},{"id":"2306.14066","title":"SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models","created":"2023-06-24","cited_by_count":28},{"id":"2305.14452","title":"Fourier Neural Operators for Arbitrary Resolution Climate Data Downscaling","created":"2023-05-23","cited_by_count":26},{"id":"2008.04679","title":"ClimAlign: Unsupervised statistical downscaling of climate variables via normalizing flows","created":"2020-08-11","cited_by_count":25},{"id":"2307.07313","title":"HEAL-SWIN: A Vision Transformer On The Sphere","created":"2023-07-14","cited_by_count":20},{"id":"2005.04988","title":"A review of radar-based nowcasting of precipitation and applicable machine learning techniques","created":"2020-05-11","cited_by_count":19},{"id":"2101.06813","title":"Fast and accurate learned multiresolution dynamical downscaling for precipitation","created":"2021-01-18","cited_by_count":18},{"id":"2307.09072","title":"Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)","created":"2023-07-18","cited_by_count":16},{"id":"2004.08340","title":"Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks","created":"2020-04-17","cited_by_count":15},{"id":"2101.02121","title":"Attention-based Convolutional Autoencoders for 3D-Variational Data Assimilation","created":"2021-01-06","cited_by_count":15},{"id":"2111.02780","title":"Flood forecasting with machine learning models in an operational framework","created":"2021-11-04","cited_by_count":15},{"id":"2210.13817","title":"Online model error correction with neural networks in the incremental 4D-Var framework","created":"2022-10-25","cited_by_count":14},{"id":"2011.06125","title":"Hurricane Forecasting: A Novel Multimodal Machine Learning Framework","created":"2020-11-11","cited_by_count":13},{"id":"2207.11417","title":"Multiscale Neural Operator: Learning Fast and Grid-independent PDE Solvers","created":"2022-07-23","cited_by_count":12},{"id":"2202.04942","title":"Spherical Transformer","created":"2022-02-10","cited_by_count":11},{"id":"2306.08754","title":"ClimSim-Online: A Large Multi-scale Dataset and Framework for Hybrid ML-physics Climate Emulation","created":"2023-06-14","cited_by_count":10},{"id":"2202.01897","title":"AtmoDist: Self-supervised Representation Learning for Atmospheric Dynamics","created":"2022-02-02","cited_by_count":8},{"id":"1910.06789","title":"Deep learning for Aerosol Forecasting","created":"2019-10-14","cited_by_count":1}],"next_cursor":null}