{"list":"other","total":133,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"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":"1609.02907","title":"Semi-Supervised Classification with Graph Convolutional Networks","created":"2016-09-09","cited_by_count":11361},{"id":"1710.10903","title":"Graph Attention Networks","created":"2017-10-30","cited_by_count":8923},{"id":"1612.00593","title":"PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation","created":"2016-12-02","cited_by_count":5248},{"id":"1409.0473","title":"Neural Machine Translation by Jointly Learning to Align and Translate","created":"2014-09-01","cited_by_count":4548},{"id":"1706.02413","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","created":"2017-06-07","cited_by_count":4401},{"id":"1301.3781","title":"Efficient Estimation of Word Representations in Vector Space","created":"2013-01-16","cited_by_count":4375},{"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":"1903.02428","title":"Fast Graph Representation Learning with PyTorch Geometric","created":"2019-03-06","cited_by_count":2434},{"id":"1607.00653","title":"node2vec: Scalable Feature Learning for Networks","created":"2016-07-03","cited_by_count":1918},{"id":"1403.6652","title":"DeepWalk: Online Learning of Social Representations","created":"2014-03-26","cited_by_count":1547},{"id":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"id":"1703.06114","title":"Deep Sets","created":"2017-03-10","cited_by_count":1421},{"id":"1611.07308","title":"Variational Graph Auto-Encoders","created":"2016-11-21","cited_by_count":1382},{"id":"1812.07035","title":"On the Continuity of Rotation Representations in Neural Networks","created":"2018-12-17","cited_by_count":1214},{"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":"1806.01973","title":"Graph Convolutional Neural Networks for Web-Scale Recommender Systems","created":"2018-06-06","cited_by_count":1138},{"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":"2006.15055","title":"Object-Centric Learning with Slot Attention","created":"2020-06-26","cited_by_count":876},{"id":"2010.03409","title":"Learning Mesh-Based Simulation with Graph Networks","created":"2020-10-07","cited_by_count":868},{"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":"2012.09688","title":"PCT: Point cloud transformer","created":"2020-12-17","cited_by_count":732},{"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":"1705.08039","title":"Poincar\\'e Embeddings for Learning Hierarchical Representations","created":"2017-05-22","cited_by_count":684},{"id":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"1410.5401","title":"Neural Turing Machines","created":"2014-10-20","cited_by_count":644},{"id":"2006.10503","title":"SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks","created":"2020-06-18","cited_by_count":607},{"id":"1706.08566","title":"SchNet: A continuous-filter convolutional neural network for modeling quantum interactions","created":"2017-06-26","cited_by_count":571},{"id":"2203.02923","title":"GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation","created":"2022-03-06","cited_by_count":516},{"id":"2011.02260","title":"Graph Neural Networks in Recommender Systems: A Survey","created":"2020-11-04","cited_by_count":494},{"id":"2111.14522","title":"Understanding over-squashing and bottlenecks on graphs via curvature","created":"2021-11-29","cited_by_count":478},{"id":"1801.10130","title":"Spherical CNNs","created":"2018-01-30","cited_by_count":456},{"id":"1806.03962","title":"Rotation Equivariant CNNs for Digital Pathology","created":"2018-06-08","cited_by_count":422},{"id":"1802.03480","title":"GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders","created":"2018-02-09","cited_by_count":383},{"id":"1802.04687","title":"Neural Relational Inference for Interacting Systems","created":"2018-02-13","cited_by_count":339},{"id":"1612.08498","title":"Steerable CNNs","created":"2016-12-27","cited_by_count":305},{"id":"1802.03690","title":"On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups","created":"2018-02-11","cited_by_count":300},{"id":"1902.04615","title":"Gauge Equivariant Convolutional Networks and the Icosahedral CNN","created":"2019-02-11","cited_by_count":267},{"id":"1903.11960","title":"Learning Discrete Structures for Graph Neural Networks","created":"2019-03-28","cited_by_count":231},{"id":"1811.02017","title":"A General Theory of Equivariant CNNs on Homogeneous Spaces","created":"2018-11-05","cited_by_count":228},{"id":"2110.02905","title":"Geometric and Physical Quantities Improve E(3) Equivariant Message Passing","created":"2021-10-06","cited_by_count":228},{"id":"2103.03212","title":"Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks","created":"2021-03-04","cited_by_count":221},{"id":"1906.05404","title":"Topology-Preserving Deep Image Segmentation","created":"2019-06-12","cited_by_count":198},{"id":"1804.00891","title":"Hyperspherical Variational Auto-Encoders","created":"2018-04-03","cited_by_count":189},{"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":"2305.01140","title":"Geometric Latent Diffusion Models for 3D Molecule Generation","created":"2023-05-02","cited_by_count":186},{"id":"1906.00957","title":"Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules","created":"2019-06-02","cited_by_count":172},{"id":"1806.09231","title":"Clebsch-Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network","created":"2018-06-24","cited_by_count":171},{"id":"1608.04233","title":"A Riemannian Network for SPD Matrix Learning","created":"2016-08-15","cited_by_count":162},{"id":"1910.01877","title":"A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology","created":"2019-10-04","cited_by_count":134},{"id":"2108.11482","title":"ETA Prediction with Graph Neural Networks in Google Maps","created":"2021-08-25","cited_by_count":130},{"id":"1805.09786","title":"Hyperbolic Attention Networks","created":"2018-05-24","cited_by_count":122},{"id":"2204.00673","title":"Learnable latent embeddings for joint behavioral and neural analysis","created":"2022-04-01","cited_by_count":117},{"id":"2005.02819","title":"Geoopt: Riemannian Optimization in PyTorch","created":"2020-05-06","cited_by_count":114},{"id":"2107.12375","title":"Geometric Deep Learning on Molecular Representations","created":"2021-07-26","cited_by_count":113},{"id":"2010.03633","title":"Simplicial Neural Networks","created":"2020-10-07","cited_by_count":110},{"id":"2012.10885","title":"LieTransformer: Equivariant self-attention for Lie Groups","created":"2020-12-20","cited_by_count":100},{"id":"1904.11088","title":"D-VAE: A Variational Autoencoder for Directed Acyclic Graphs","created":"2019-04-24","cited_by_count":98},{"id":"2302.09048","title":"MiDi: Mixed Graph and 3D Denoising Diffusion for Molecule Generation","created":"2023-02-17","cited_by_count":98},{"id":"2206.00606","title":"Topological Deep Learning: Going Beyond Graph Data","created":"2022-06-01","cited_by_count":97},{"id":"1603.03236","title":"Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation","created":"2016-03-10","cited_by_count":94},{"id":"1804.03393","title":"Roto-Translation Covariant Convolutional Networks for Medical Image Analysis","created":"2018-04-10","cited_by_count":92},{"id":"1807.05044","title":"Random Walks on Simplicial Complexes and the normalized Hodge 1-Laplacian","created":"2018-07-13","cited_by_count":86},{"id":"2002.04999","title":"Differentiable Graph Module (DGM) for Graph Convolutional Networks","created":"2020-02-11","cited_by_count":86},{"id":"2006.10160","title":"Mat\\'ern Gaussian processes on Riemannian manifolds","created":"2020-06-17","cited_by_count":82},{"id":"2104.04883","title":"Graph Representation Learning in Biomedicine","created":"2021-04-11","cited_by_count":80},{"id":"2405.15943","title":"Transformers represent belief state geometry in their residual stream","created":"2024-05-24","cited_by_count":77},{"id":"2103.12608","title":"DIG: A Turnkey Library for Diving into Graph Deep Learning Research","created":"2021-03-23","cited_by_count":76},{"id":"2103.09992","title":"Topology-Aware Segmentation Using Discrete Morse Theory","created":"2021-03-18","cited_by_count":75},{"id":"2011.00387","title":"Be More with Less: Hypergraph Attention Networks for Inductive Text Classification","created":"2020-11-01","cited_by_count":74},{"id":"1805.08308","title":"geomstats: a Python Package for Riemannian Geometry in Machine Learning","created":"2018-05-21","cited_by_count":71},{"id":"2010.00743","title":"Cell Complex Neural Networks","created":"2020-10-02","cited_by_count":67},{"id":"1807.04689","title":"Explorations in Homeomorphic Variational Auto-Encoding","created":"2018-07-12","cited_by_count":65},{"id":"2302.02672","title":"Identifiability of latent-variable and structural-equation models: from linear to nonlinear","created":"2023-02-06","cited_by_count":58},{"id":"1608.03012","title":"Fr\\'echet Regression for Random Objects with Euclidean Predictors","created":"2016-08-10","cited_by_count":57},{"id":"1809.02745","title":"Molecular Hypergraph Grammar with its Application to Molecular Optimization","created":"2018-09-08","cited_by_count":56},{"id":"2011.12747","title":"Symmetry-Aware Actor-Critic for 3D Molecular Design","created":"2020-11-25","cited_by_count":55},{"id":"2302.04973","title":"Invariant Slot Attention: Object Discovery with Slot-Centric Reference Frames","created":"2023-02-09","cited_by_count":51},{"id":"2305.18415","title":"Geometric Algebra Transformer","created":"2023-05-28","cited_by_count":48},{"id":"2304.10031","title":"Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks","created":"2023-04-20","cited_by_count":43},{"id":"2310.02970","title":"Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space","created":"2023-10-04","cited_by_count":41},{"id":"2004.07399","title":"Representation Learning of Histopathology Images using Graph Neural Networks","created":"2020-04-16","cited_by_count":40},{"id":"2209.08179","title":"Cell Attention Networks","created":"2022-09-16","cited_by_count":35},{"id":"2004.03037","title":"Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images","created":"2020-04-06","cited_by_count":33},{"id":"2002.08725","title":"Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis","created":"2020-02-20","cited_by_count":32},{"id":"2402.15332","title":"Position: Categorical Deep Learning is an Algebraic Theory of All Architectures","created":"2024-02-23","cited_by_count":32},{"id":"2110.05614","title":"Signal Processing on Cell Complexes","created":"2021-10-11","cited_by_count":31},{"id":"2006.12982","title":"Disentangling by Subspace Diffusion","created":"2020-06-23","cited_by_count":28},{"id":"2112.03321","title":"Noether Networks: Meta-Learning Useful Conserved Quantities","created":"2021-12-06","cited_by_count":26},{"id":"2406.06642","title":"TopoBench: A Framework for Benchmarking Topological Deep Learning","created":"2024-06-09","cited_by_count":24},{"id":"2305.07100","title":"E(n) Equivariant Message Passing Simplicial Networks","created":"2023-05-11","cited_by_count":23},{"id":"2402.02441","title":"TopoX: A Suite of Python Packages for Machine Learning on Topological Domains","created":"2024-02-04","cited_by_count":22},{"id":"2402.10011","title":"Clifford Group Equivariant Simplicial Message Passing Networks","created":"2024-02-15","cited_by_count":22},{"id":"2405.15429","title":"E(n) Equivariant Topological Neural Networks","created":"2024-05-24","cited_by_count":22},{"id":"2102.02828","title":"Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs","created":"2021-02-04","cited_by_count":18}],"next_cursor":"100"}