{"list":"other","total":74,"rows":[{"id":"1512.03385","title":"Deep Residual Learning for Image Recognition","created":"2015-12-10","cited_by_count":52096},{"id":"2006.11239","title":"Denoising Diffusion Probabilistic Models","created":"2020-06-19","cited_by_count":21076},{"id":"2205.11487","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","created":"2022-05-23","cited_by_count":6355},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1608.03983","title":"SGDR: Stochastic Gradient Descent with Warm Restarts","created":"2016-08-13","cited_by_count":5045},{"id":"1502.01852","title":"Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification","created":"2015-02-06","cited_by_count":3611},{"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":"2010.08895","title":"Fourier Neural Operator for Parametric Partial Differential Equations","created":"2020-10-18","cited_by_count":2908},{"id":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1801.07829","title":"Dynamic Graph CNN for Learning on Point Clouds","created":"2018-01-24","cited_by_count":2223},{"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":"1803.08494","title":"Group Normalization","created":"2018-03-22","cited_by_count":1524},{"id":"1806.01261","title":"Relational inductive biases, deep learning, and graph networks","created":"2018-06-04","cited_by_count":1360},{"id":"2104.13478","title":"Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges","created":"2021-04-27","cited_by_count":1167},{"id":"1602.07576","title":"Group Equivariant Convolutional Networks","created":"2016-02-24","cited_by_count":1165},{"id":"1611.08097","title":"Geometric deep learning: going beyond Euclidean data","created":"2016-11-24","cited_by_count":1048},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"id":"2002.09405","title":"Learning to Simulate Complex Physics with Graph Networks","created":"2020-02-21","cited_by_count":880},{"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":"1802.08219","title":"Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds","created":"2018-02-22","cited_by_count":746},{"id":"1906.01563","title":"Hamiltonian Neural Networks","created":"2019-06-04","cited_by_count":712},{"id":"2101.03164","title":"E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials","created":"2021-01-08","cited_by_count":708},{"id":"1710.09829","title":"Dynamic Routing Between Capsules","created":"2017-10-26","cited_by_count":689},{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"1505.00853","title":"Empirical Evaluation of Rectified Activations in Convolutional Network","created":"2015-05-05","cited_by_count":519},{"id":"2206.07697","title":"MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields","created":"2022-06-15","cited_by_count":474},{"id":"2102.01010","title":"Machine learning accelerated computational fluid dynamics","created":"2021-01-28","cited_by_count":471},{"id":"2207.05209","title":"Fourier Neural Operator with Learned Deformations for PDEs on General Geometries","created":"2022-07-11","cited_by_count":427},{"id":"2202.03376","title":"Message Passing Neural PDE Solvers","created":"2022-02-07","cited_by_count":386},{"id":"1807.02547","title":"3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data","created":"2018-07-06","cited_by_count":354},{"id":"2301.10343","title":"ClimaX: A foundation model for weather and climate","created":"2023-01-24","cited_by_count":337},{"id":"2212.12794","title":"GraphCast: Learning skillful medium-range global weather forecasting","created":"2022-12-24","cited_by_count":267},{"id":"1806.01411","title":"FlowNet3D: Learning Scene Flow in 3D Point Clouds","created":"2018-06-04","cited_by_count":242},{"id":"1705.09792","title":"Deep Complex Networks","created":"2017-05-27","cited_by_count":217},{"id":"2209.15616","title":"Towards Multi-spatiotemporal-scale Generalized PDE Modeling","created":"2022-09-30","cited_by_count":197},{"id":"2104.09459","title":"A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups","created":"2021-04-19","cited_by_count":187},{"id":"1909.12077","title":"Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control","created":"2019-09-26","cited_by_count":184},{"id":"2004.13145","title":"PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parameterized Steady-State PDEs on Irregular Domain","created":"2020-04-21","cited_by_count":174},{"id":"2207.09453","title":"e3nn: Euclidean Neural Networks","created":"2022-07-18","cited_by_count":174},{"id":"2211.02556","title":"Pangu-Weather: A 3D High-Resolution Model for Fast and Accurate Global Weather Forecast","created":"2022-11-03","cited_by_count":166},{"id":"1710.06012","title":"VAMPnets: Deep learning of molecular kinetics","created":"2017-10-16","cited_by_count":136},{"id":"2001.07457","title":"Learning to Control PDEs with Differentiable Physics","created":"2020-01-21","cited_by_count":132},{"id":"1911.12207","title":"Orthogonal Convolutional Neural Networks","created":"2019-11-27","cited_by_count":123},{"id":"1711.06464","title":"A unified deep artificial neural network approach to partial differential equations in complex geometries","created":"2017-11-17","cited_by_count":122},{"id":"1910.09165","title":"MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences","created":"2019-10-21","cited_by_count":117},{"id":"1811.11424","title":"MeshNet: Mesh Neural Network for 3D Shape Representation","created":"2018-11-28","cited_by_count":95},{"id":"2209.04934","title":"Clifford Neural Layers for PDE Modeling","created":"2022-09-08","cited_by_count":92},{"id":"2212.07564","title":"AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions","created":"2022-12-15","cited_by_count":82},{"id":"2202.07643","title":"Lie Point Symmetry Data Augmentation for Neural PDE Solvers","created":"2022-02-15","cited_by_count":77},{"id":"2106.06020","title":"Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds","created":"2021-06-10","cited_by_count":73},{"id":"1803.05649","title":"Sylvester Normalizing Flows for Variational Inference","created":"2018-03-15","cited_by_count":72},{"id":"2106.02347","title":"SE(3)-equivariant prediction of molecular wavefunctions and electronic densities","created":"2021-06-04","cited_by_count":67},{"id":"1612.00188","title":"Efficient Orthogonal Parametrisation of Recurrent Neural Networks Using Householder Reflections","created":"2016-12-01","cited_by_count":57},{"id":"2008.08461","title":"Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties","created":"2020-08-19","cited_by_count":51},{"id":"1606.02378","title":"SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks","created":"2016-06-08","cited_by_count":49},{"id":"2010.12455","title":"Primal-Dual Mesh Convolutional Neural Networks","created":"2020-10-23","cited_by_count":44},{"id":"1904.08991","title":"Physical Symmetries Embedded in Neural Networks","created":"2019-04-18","cited_by_count":40},{"id":"2007.02005","title":"Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks","created":"2020-07-04","cited_by_count":30},{"id":"2107.01214","title":"Truncated Marginal Neural Ratio Estimation","created":"2021-07-02","cited_by_count":30},{"id":"2106.11299","title":"Boundary Graph Neural Networks for 3D Simulations","created":"2021-06-21","cited_by_count":24},{"id":"2110.14961","title":"Roto-translated Local Coordinate Frames For Interacting Dynamical Systems","created":"2021-10-28","cited_by_count":24},{"id":"2110.09783","title":"Spatial-Temporal Transformer for 3D Point Cloud Sequences","created":"2021-10-19","cited_by_count":22},{"id":"2006.01910","title":"The Convolution Exponential and Generalized Sylvester Flows","created":"2020-06-02","cited_by_count":18},{"id":"1910.00024","title":"Neural Canonical Transformation with Symplectic Flows","created":"2019-09-30","cited_by_count":16},{"id":"2101.04117","title":"A Bayesian neural network predicts the dissolution of compact planetary systems","created":"2021-01-11","cited_by_count":15},{"id":"2107.03771","title":"Graded Symmetry Groups: Plane and Simple","created":"2021-07-08","cited_by_count":11},{"id":"2110.02393","title":"Geometric Algebra Attention Networks for Small Point Clouds","created":"2021-10-05","cited_by_count":10},{"id":"2008.05149","title":"ASAP-Net: Attention and Structure Aware Point Cloud Sequence Segmentation","created":"2020-08-12","cited_by_count":8},{"id":"2006.07360","title":"AlgebraNets","created":"2020-06-12","cited_by_count":7},{"id":"2210.02083","title":"Multi-View Independent Component Analysis with Shared and Individual Sources","created":"2022-10-05","cited_by_count":7},{"id":"1709.08491","title":"Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks","created":"2017-09-25","cited_by_count":3},{"id":"2107.13349","title":"Self-Supervised Inference in State-Space Models","created":"2021-07-28","cited_by_count":3},{"id":"2206.07496","title":"Normalization, Square Roots, and the Exponential and Logarithmic Maps in Geometric Algebras of Less than 6D","created":"2022-05-11","cited_by_count":1}],"next_cursor":null}