{"list":"other","total":70,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"1912.01703","title":"PyTorch: An Imperative Style, High-Performance Deep Learning Library","created":"2019-12-03","cited_by_count":20305},{"id":"2203.02155","title":"Training language models to follow instructions with human feedback","created":"2022-03-04","cited_by_count":15971},{"id":"2011.13456","title":"Score-Based Generative Modeling through Stochastic Differential Equations","created":"2020-11-26","cited_by_count":9122},{"id":"1503.03585","title":"Deep Unsupervised Learning using Nonequilibrium Thermodynamics","created":"2015-03-12","cited_by_count":7058},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"1901.00596","title":"A Comprehensive Survey on Graph Neural Networks","created":"2019-01-03","cited_by_count":2919},{"id":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"1806.07572","title":"Neural Tangent Kernel: Convergence and Generalization in Neural Networks","created":"2018-06-20","cited_by_count":2621},{"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":"1812.08434","title":"Graph Neural Networks: A Review of Methods and Applications","created":"2018-12-20","cited_by_count":1576},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1811.03962","title":"A Convergence Theory for Deep Learning via Over-Parameterization","created":"2018-11-09","cited_by_count":787},{"id":"2003.03485","title":"Neural Operator: Graph Kernel Network for Partial Differential Equations","created":"2020-03-07","cited_by_count":734},{"id":"1708.07469","title":"DGM: A deep learning algorithm for solving partial differential equations","created":"2017-08-24","cited_by_count":678},{"id":"2007.14527","title":"When and why PINNs fail to train: A neural tangent kernel perspective","created":"2020-07-28","cited_by_count":634},{"id":"1907.04502","title":"DeepXDE: A deep learning library for solving differential equations","created":"2019-07-10","cited_by_count":619},{"id":"2201.05624","title":"Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next","created":"2022-01-14","cited_by_count":608},{"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":"1610.01145","title":"Error bounds for approximations with deep ReLU networks","created":"2016-10-03","cited_by_count":574},{"id":"1811.03804","title":"Gradient Descent Finds Global Minima of Deep Neural Networks","created":"2018-11-09","cited_by_count":574},{"id":"1905.11481","title":"AI Feynman: a Physics-Inspired Method for Symbolic Regression","created":"2019-05-27","cited_by_count":548},{"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":"2207.05209","title":"Fourier Neural Operator with Learned Deformations for PDEs on General Geometries","created":"2022-07-11","cited_by_count":427},{"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":"2302.14376","title":"GNOT: A General Neural Operator Transformer for Operator Learning","created":"2023-02-28","cited_by_count":336},{"id":"2309.00583","title":"Geometry-Informed Neural Operator for Large-Scale 3D PDEs","created":"2023-09-01","cited_by_count":303},{"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":"2109.13459","title":"Multiwavelet-based Operator Learning for Differential Equations","created":"2021-09-28","cited_by_count":234},{"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":"2211.13449","title":"Fast Sampling of Diffusion Models via Operator Learning","created":"2022-11-24","cited_by_count":175},{"id":"2006.10782","title":"AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity","created":"2020-06-18","cited_by_count":144},{"id":"2308.08468","title":"An Expert's Guide to Training Physics-informed Neural Networks","created":"2023-08-16","cited_by_count":126},{"id":"2207.05748","title":"Physics-Informed Deep Neural Operator Networks","created":"2022-07-08","cited_by_count":113},{"id":"2201.01032","title":"Learning Operators with Coupled Attention","created":"2022-01-04","cited_by_count":90},{"id":"1312.5851","title":"Fast Training of Convolutional Networks through FFTs","created":"2013-12-20","cited_by_count":89},{"id":"2306.08827","title":"PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs","created":"2023-06-15","cited_by_count":87},{"id":"2110.01654","title":"Improved architectures and training algorithms for deep operator networks","created":"2021-10-04","cited_by_count":86},{"id":"2205.02191","title":"Wavelet neural operator: a neural operator for parametric partial differential equations","created":"2022-05-04","cited_by_count":78},{"id":"2004.01902","title":"Rational neural networks","created":"2020-04-04","cited_by_count":75},{"id":"2210.01741","title":"Neural Conservation Laws: A Divergence-Free Perspective","created":"2022-10-04","cited_by_count":70},{"id":"2203.08205","title":"Learning Deep Implicit Fourier Neural Operators (IFNOs) with Applications to Heterogeneous Material Modeling","created":"2022-03-15","cited_by_count":65},{"id":"2205.11393","title":"Generic bounds on the approximation error for physics-informed (and) operator learning","created":"2022-05-23","cited_by_count":65},{"id":"2101.07206","title":"DeepGreen: Deep Learning of Green's Functions for Nonlinear Boundary Value Problems","created":"2020-12-31","cited_by_count":62},{"id":"2212.06347","title":"Reliable extrapolation of deep neural operators informed by physics or sparse observations","created":"2022-12-13","cited_by_count":62},{"id":"1912.02968","title":"Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport","created":"2019-12-06","cited_by_count":61},{"id":"2108.12515","title":"Convergence Rates for Learning Linear Operators from Noisy Data","created":"2021-08-27","cited_by_count":55},{"id":"2105.00266","title":"Data-driven discovery of Green's functions with human-understandable deep learning","created":"2021-05-01","cited_by_count":44},{"id":"2206.10745","title":"Derivative-Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning","created":"2022-06-21","cited_by_count":44},{"id":"2204.12670","title":"SVD Perspectives for Augmenting DeepONet Flexibility and Interpretability","created":"2022-04-27","cited_by_count":35},{"id":"2301.11509","title":"Out-of-distributional risk bounds for neural operators with applications to the Helmholtz equation","created":"2023-01-27","cited_by_count":32},{"id":"2202.07176","title":"DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories","created":"2022-02-15","cited_by_count":30},{"id":"2102.00491","title":"Learning elliptic partial differential equations with randomized linear algebra","created":"2021-01-31","cited_by_count":29},{"id":"2204.12789","title":"Learning Green's functions associated with time-dependent partial differential equations","created":"2022-04-27","cited_by_count":26},{"id":"2302.12888","title":"Elliptic PDE learning is provably data-efficient","created":"2023-02-24","cited_by_count":26},{"id":"1706.02205","title":"Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity","created":"2017-06-07","cited_by_count":24},{"id":"2311.00212","title":"A Unified Framework to Enforce, Discover, and Promote Symmetry in Machine Learning","created":"2023-11-01","cited_by_count":22},{"id":"2302.09808","title":"RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator","created":"2023-02-20","cited_by_count":19},{"id":"2305.11111","title":"PPDONet: Deep Operator Networks for Fast Prediction of Steady-State Solutions in Disk-Planet Systems","created":"2023-05-18","cited_by_count":19},{"id":"1906.06782","title":"Meta-learning Pseudo-differential Operators with Deep Neural Networks","created":"2019-06-16","cited_by_count":17},{"id":"2204.13247","title":"BI-GreenNet: Learning Green's functions by boundary integral network","created":"2022-04-28","cited_by_count":13},{"id":"2209.14430","title":"Minimax Optimal Kernel Operator Learning via Multilevel Training","created":"2022-09-28","cited_by_count":13},{"id":"2105.13052","title":"A generalization of the randomized singular value decomposition","created":"2021-05-27","cited_by_count":6},{"id":"2306.02925","title":"Deep Generalized Green's Functions","created":"2023-06-05","cited_by_count":5}],"next_cursor":null}