{"list":"other","total":68,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"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":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"2111.06377","title":"Masked Autoencoders Are Scalable Vision Learners","created":"2021-11-11","cited_by_count":7403},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"2104.09864","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","created":"2021-04-20","cited_by_count":4703},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1606.08415","title":"Gaussian Error Linear Units (GELUs)","created":"2016-06-27","cited_by_count":3502},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1409.3215","title":"Sequence to Sequence Learning with Neural Networks","created":"2014-09-10","cited_by_count":2986},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"2006.10739","title":"Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains","created":"2020-06-18","cited_by_count":2200},{"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":"2009.14794","title":"Rethinking Attention with Performers","created":"2020-09-30","cited_by_count":1601},{"id":"1607.08022","title":"Instance Normalization: The Missing Ingredient for Fast Stylization","created":"2016-07-27","cited_by_count":1198},{"id":"2103.03206","title":"Perceiver: General Perception with Iterative Attention","created":"2021-03-04","cited_by_count":1104},{"id":"1508.04025","title":"Effective Approaches to Attention-based Neural Machine Translation","created":"2015-08-17","cited_by_count":1026},{"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":"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":"1312.6120","title":"Exact solutions to the nonlinear dynamics of learning in deep linear neural networks","created":"2013-12-20","cited_by_count":763},{"id":"2003.03485","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","created":"2020-03-07","cited_by_count":734},{"id":"1612.08083","title":"Language Modeling with Gated Convolutional Networks","created":"2016-12-23","cited_by_count":702},{"id":"1708.07469","title":"DGM: A deep learning algorithm for solving partial differential equations","created":"2017-08-24","cited_by_count":678},{"id":"1410.5401","title":"Neural Turing Machines","created":"2014-10-20","cited_by_count":644},{"id":"2109.01050","title":"Characterizing possible failure modes in physics-informed neural networks","created":"2021-09-02","cited_by_count":643},{"id":"1907.04502","title":"DeepXDE: A deep learning library for solving differential equations","created":"2019-07-10","cited_by_count":619},{"id":"1710.00211","title":"The Deep Ritz method: A deep learning-based numerical algorithm for solving variational problems","created":"2017-09-30","cited_by_count":583},{"id":"2103.10974","title":"Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets","created":"2021-03-19","cited_by_count":540},{"id":"2111.03794","title":"Physics-Informed Neural Operator for Learning Partial Differential Equations","created":"2021-11-06","cited_by_count":527},{"id":"1712.09707","title":"Deep learning for universal linear embeddings of nonlinear dynamics","created":"2017-12-27","cited_by_count":494},{"id":"2108.08481","title":"Neural Operator: Learning Maps Between Function Spaces","created":"2021-08-19","cited_by_count":414},{"id":"2202.03376","title":"Message Passing Neural PDE Solvers","created":"2022-02-07","cited_by_count":386},{"id":"1905.11075","title":"Machine Learning for Fluid Mechanics","created":"2019-05-27","cited_by_count":385},{"id":"2006.09535","title":"Multipole Graph Neural Operator for Parametric Partial Differential Equations","created":"2020-06-16","cited_by_count":349},{"id":"2105.14995","title":"Choose a Transformer: Fourier or Galerkin","created":"2021-05-31","cited_by_count":343},{"id":"2109.03697","title":"U-FNO -- An enhanced Fourier neural operator-based deep-learning model for multiphase flow","created":"2021-09-03","cited_by_count":297},{"id":"2012.10047","title":"On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks","created":"2020-12-18","cited_by_count":284},{"id":"2005.03180","title":"Model Reduction and Neural Networks for Parametric PDEs","created":"2020-05-07","cited_by_count":258},{"id":"2111.13802","title":"Factorized Fourier Neural Operators","created":"2021-11-27","cited_by_count":249},{"id":"2109.13459","title":"Multiwavelet-based Operator Learning for Differential Equations","created":"2021-09-28","cited_by_count":234},{"id":"2204.11127","title":"U-NO: U-shaped Neural Operators","created":"2022-04-23","cited_by_count":230},{"id":"1911.08655","title":"Towards Physics-informed Deep Learning for Turbulent Flow Prediction","created":"2019-11-20","cited_by_count":204},{"id":"1708.07120","title":"Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates","created":"2017-08-23","cited_by_count":199},{"id":"1801.06879","title":"Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification","created":"2018-01-21","cited_by_count":199},{"id":"1710.04340","title":"Learning Koopman Invariant Subspaces for Dynamic Mode Decomposition","created":"2017-10-12","cited_by_count":158},{"id":"1905.13166","title":"Prediction of Aerodynamic Flow Fields Using Convolutional Neural Networks","created":"2019-05-30","cited_by_count":145},{"id":"2007.04439","title":"Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction","created":"2020-07-08","cited_by_count":138},{"id":"2010.03957","title":"Transformers for Modeling Physical Systems","created":"2020-10-04","cited_by_count":106},{"id":"1802.10123","title":"Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow","created":"2018-02-27","cited_by_count":97},{"id":"2005.10224","title":"The Random Feature Model for Input-Output Maps between Banach Spaces","created":"2020-05-20","cited_by_count":90},{"id":"2201.01032","title":"Learning Operators with Coupled Attention","created":"2022-01-04","cited_by_count":90},{"id":"2110.01654","title":"Improved architectures and training algorithms for deep operator networks","created":"2021-10-04","cited_by_count":86},{"id":"2201.09113","title":"Predicting Physics in Mesh-reduced Space with Temporal Attention","created":"2022-01-22","cited_by_count":84},{"id":"2205.02191","title":"Wavelet neural operator: a neural operator for parametric partial differential equations","created":"2022-05-04","cited_by_count":78},{"id":"1906.01200","title":"Learning Neural PDE Solvers with Convergence Guarantees","created":"2019-06-04","cited_by_count":77},{"id":"1805.07472","title":"Deep Dynamical Modeling and Control of Unsteady Fluid Flows","created":"2018-05-18","cited_by_count":68},{"id":"1910.08264","title":"Learning Compositional Koopman Operators for Model-Based Control","created":"2019-10-18","cited_by_count":67},{"id":"1906.03663","title":"Physics-Informed Probabilistic Learning of Linear Embeddings of Non-linear Dynamics With Guaranteed Stability","created":"2019-06-09","cited_by_count":53},{"id":"2006.08956","title":"Learning continuous-time PDEs from sparse data with graph neural networks","created":"2020-06-16","cited_by_count":53},{"id":"2107.04556","title":"Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid Simulations","created":"2021-07-09","cited_by_count":36},{"id":"2112.03383","title":"Graph Neural Networks Accelerated Molecular Dynamics","created":"2021-12-06","cited_by_count":33},{"id":"2012.02232","title":"Graph Convolutional Neural Networks for Body Force Prediction","created":"2020-12-03","cited_by_count":16},{"id":"2011.14965","title":"A Deep Learning Approach for Predicting Spatiotemporal Dynamics From Sparsely Observed Data","created":"2020-11-30","cited_by_count":3}],"next_cursor":null}