{"list":"other","total":55,"rows":[{"id":"1706.03762","title":"Attention Is All You Need","created":"2017-06-12","cited_by_count":60361},{"id":"1810.04805","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","created":"2018-10-11","cited_by_count":40399},{"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":"2212.09748","title":"Scalable Diffusion Models with Transformers","created":"2022-12-19","cited_by_count":6054},{"id":"2104.09864","title":"RoFormer: Enhanced Transformer with Rotary Position Embedding","created":"2021-04-20","cited_by_count":4703},{"id":"1704.01212","title":"Neural Message Passing for Quantum Chemistry","created":"2017-04-04","cited_by_count":3487},{"id":"2006.03654","title":"DeBERTa: Decoding-enhanced BERT with Disentangled Attention","created":"2020-06-05","cited_by_count":2181},{"id":"1901.02860","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","created":"2019-01-09","cited_by_count":1850},{"id":"2009.14794","title":"Rethinking Attention with Performers","created":"2020-09-30","cited_by_count":1601},{"id":"2011.00931","title":"Point Transformer","created":"2020-11-02","cited_by_count":1470},{"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":"1803.02155","title":"Self-Attention with Relative Position Representations","created":"2018-03-06","cited_by_count":1120},{"id":"2102.09844","title":"E(n) Equivariant Graph Neural Networks","created":"2021-02-19","cited_by_count":965},{"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":"2306.15595","title":"Extending Context Window of Large Language Models via Positional Interpolation","created":"2023-06-27","cited_by_count":740},{"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":"2203.17003","title":"Equivariant Diffusion for Molecule Generation in 3D","created":"2022-03-31","cited_by_count":677},{"id":"2111.14819","title":"Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling","created":"2021-11-29","cited_by_count":653},{"id":"2006.10503","title":"SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks","created":"2020-06-18","cited_by_count":607},{"id":"1908.04616","title":"Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data","created":"2019-08-13","cited_by_count":532},{"id":"2102.03150","title":"Equivariant message passing for the prediction of tensorial properties and molecular spectra","created":"2021-02-05","cited_by_count":441},{"id":"2204.07118","title":"DeiT III: Revenge of the ViT","created":"2022-04-14","cited_by_count":419},{"id":"1612.08498","title":"Steerable CNNs","created":"2016-12-27","cited_by_count":305},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264},{"id":"2011.14115","title":"Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules","created":"2020-11-28","cited_by_count":255},{"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":"2306.12059","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","created":"2023-06-21","cited_by_count":204},{"id":"2305.01140","title":"Geometric Latent Diffusion Models for 3D Molecule Generation","created":"2023-05-02","cited_by_count":186},{"id":"2202.02541","title":"TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials","created":"2022-02-05","cited_by_count":182},{"id":"2102.05013","title":"Spherical Message Passing for 3D Graph Networks","created":"2021-02-09","cited_by_count":168},{"id":"2110.03336","title":"Frame Averaging for Invariant and Equivariant Network Design","created":"2021-10-07","cited_by_count":168},{"id":"2310.02391","title":"SE(3)-Stochastic Flow Matching for Protein Backbone Generation","created":"2023-10-03","cited_by_count":150},{"id":"1909.12057","title":"B-Spline CNNs on Lie Groups","created":"2019-09-26","cited_by_count":97},{"id":"2206.08917","title":"The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts","created":"2022-06-17","cited_by_count":95},{"id":"2503.03965","title":"All-atom Diffusion Transformers: Unified generative modelling of molecules and materials","created":"2025-03-05","cited_by_count":75},{"id":"2010.00977","title":"Group Equivariant Stand-Alone Self-Attention For Vision","created":"2020-10-02","cited_by_count":63},{"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":"2410.24169","title":"The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains","created":"2024-10-31","cited_by_count":40},{"id":"2401.10216","title":"Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products","created":"2024-01-18","cited_by_count":36},{"id":"2212.02229","title":"GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction","created":"2022-11-18","cited_by_count":28},{"id":"2206.04176","title":"VN-Transformer: Rotation-Equivariant Attention for Vector Neurons","created":"2022-06-08","cited_by_count":27},{"id":"2402.14730","title":"Clifford-Steerable Convolutional Neural Networks","created":"2024-02-22","cited_by_count":25},{"id":"2202.09891","title":"Equivariant Graph Attention Networks for Molecular Property Prediction","created":"2022-02-20","cited_by_count":20},{"id":"2406.05753","title":"Grounding Continuous Representations in Geometry: Equivariant Neural Fields","created":"2024-06-09","cited_by_count":19},{"id":"2406.06660","title":"Space-Time Continuous PDE Forecasting using Equivariant Neural Fields","created":"2024-06-10","cited_by_count":18},{"id":"2503.21985","title":"Improving Equivariant Networks with Probabilistic Symmetry Breaking","created":"2025-03-27","cited_by_count":16},{"id":"2412.08541","title":"Euclidean Fast Attention -- Machine Learning Global Atomic Representations at Linear Cost","created":"2024-12-11","cited_by_count":12},{"id":"2501.01999","title":"Probing Equivariance and Symmetry Breaking in Convolutional Networks","created":"2025-01-01","cited_by_count":12},{"id":"2405.15932","title":"Steerable Transformers for Volumetric Data","created":"2024-05-24","cited_by_count":8},{"id":"2502.05169","title":"Flopping for FLOPs: Leveraging equivariance for computational efficiency","created":"2025-02-07","cited_by_count":8},{"id":"2410.20516","title":"A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing","created":"2024-10-27","cited_by_count":5},{"id":"2504.20974","title":"Equivariant non-linear maps for neural networks on homogeneous spaces","created":"2025-04-29","cited_by_count":4},{"id":"2505.22560","title":"Geometric Hyena Networks for Large-scale Equivariant Learning","created":"2025-05-28","cited_by_count":4}],"next_cursor":null}