{"total":124,"rows":[{"id":"2202.13013","title":"Sign and Basis Invariant Networks for Spectral Graph Representation Learning","created":"2022-02-25","cited_by_count":179,"area":null},{"id":"2207.09453","title":"e3nn: Euclidean Neural Networks","created":"2022-07-18","cited_by_count":174,"area":null},{"id":"2211.06489","title":"Equivariance with Learned Canonicalization Functions","created":"2022-11-11","cited_by_count":109,"area":null},{"id":"2301.09308","title":"On the Expressive Power of Geometric Graph Neural Networks","created":"2023-01-23","cited_by_count":90,"area":null},{"id":"2210.08566","title":"Theory for Equivariant Quantum Neural Networks","created":"2022-10-16","cited_by_count":62,"area":null},{"id":"2202.07230","title":"Geometrically Equivariant Graph Neural Networks: A Survey","created":"2022-02-15","cited_by_count":56,"area":null},{"id":"2107.01272","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","created":"2021-07-02","cited_by_count":44,"area":null},{"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,"area":null},{"id":"2210.06876","title":"Learning Physical Dynamics with Subequivariant Graph Neural Networks","created":"2022-10-13","cited_by_count":39,"area":null},{"id":"2205.02956","title":"Low Dimensional Invariant Embeddings for Universal Geometric Learning","created":"2022-05-05","cited_by_count":38,"area":null},{"id":"2405.14806","title":"Lorentz-Equivariant Geometric Algebra Transformers for High-Energy Physics","created":"2024-05-23","cited_by_count":38,"area":null},{"id":"2202.01356","title":"Direct Molecular Conformation Generation","created":"2022-02-03","cited_by_count":30,"area":null},{"id":"2210.06036","title":"Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics","created":"2022-10-12","cited_by_count":30,"area":null},{"id":"2303.14269","title":"The Exact Sample Complexity Gain from Invariances for Kernel Regression","created":"2023-03-24","cited_by_count":28,"area":null},{"id":"2403.00485","title":"A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applications","created":"2024-03-01","cited_by_count":28,"area":null},{"id":"2411.00446","title":"A Lorentz-Equivariant Transformer for All of the LHC","created":"2024-11-01","cited_by_count":28,"area":null},{"id":"2211.00454","title":"PELICAN: Permutation Equivariant and Lorentz Invariant or Covariant Aggregator Network for Particle Physics","created":"2022-11-01","cited_by_count":26,"area":null},{"id":"2402.14730","title":"Clifford-Steerable Convolutional Neural Networks","created":"2024-02-22","cited_by_count":25,"area":null},{"id":"2211.15420","title":"Equivariant Networks for Crystal Structures","created":"2022-11-15","cited_by_count":24,"area":null},{"id":"2307.16506","title":"Explainable Equivariant Neural Networks for Particle Physics: PELICAN","created":"2023-07-31","cited_by_count":24,"area":null},{"id":"2412.11569","title":"The dark side of the forces: assessing non-conservative force models for atomistic machine learning","created":"2024-12-16","cited_by_count":24,"area":null},{"id":"2408.13242","title":"Improving Equivariant Model Training via Constraint Relaxation","created":"2024-08-23","cited_by_count":23,"area":null},{"id":"2302.05743","title":"Is Distance Matrix Enough for Geometric Deep Learning?","created":"2023-02-11","cited_by_count":22,"area":null},{"id":"2306.09375","title":"Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials","created":"2023-06-15","cited_by_count":22,"area":null},{"id":"2410.04207","title":"Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models","created":"2024-10-05","cited_by_count":22,"area":null},{"id":"2209.05582","title":"Graph Neural Networks for Molecules","created":"2022-09-12","cited_by_count":21,"area":null},{"id":"2406.07598","title":"Equivariance via Minimal Frame Averaging for More Symmetries and Efficiency","created":"2024-06-11","cited_by_count":19,"area":null},{"id":"2205.14039","title":"Group-invariant max filtering","created":"2022-05-27","cited_by_count":18,"area":null},{"id":"2204.00887","title":"Dimensionless machine learning: Imposing exact units equivariance","created":"2022-04-02","cited_by_count":17,"area":null},{"id":"2212.08630","title":"Brauer's Group Equivariant Neural Networks","created":"2022-12-16","cited_by_count":17,"area":null},{"id":"2308.10436","title":"Approximately Equivariant Graph Networks","created":"2023-08-21","cited_by_count":16,"area":null},{"id":"2111.15341","title":"ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds","created":"2021-11-30","cited_by_count":15,"area":null},{"id":"2209.11661","title":"Exact conservation laws for neural network integrators of dynamical systems","created":"2022-09-23","cited_by_count":15,"area":null},{"id":"2312.02339","title":"Expressive Sign Equivariant Networks for Spectral Geometric Learning","created":"2023-12-04","cited_by_count":15,"area":null},{"id":"2404.07194","title":"VN-EGNN: E(3)-Equivariant Graph Neural Networks with Virtual Nodes Enhance Protein Binding Site Identification","created":"2024-04-10","cited_by_count":15,"area":null},{"id":"2208.00716","title":"Graph Neural Network with Local Frame for Molecular Potential Energy Surface","created":"2022-08-01","cited_by_count":14,"area":null},{"id":"2201.12212","title":"M\\\"obius Convolutions for Spherical CNNs","created":"2022-01-28","cited_by_count":13,"area":null},{"id":"2209.14991","title":"Machine learning and invariant theory","created":"2022-09-29","cited_by_count":13,"area":null},{"id":"2501.01999","title":"Probing Equivariance and Symmetry Breaking in Convolutional Networks","created":"2025-01-01","cited_by_count":12,"area":null},{"id":"2503.19300","title":"UniMoMo: Unified Generative Modeling of 3D Molecules for De Novo Binder Design","created":"2025-03-25","cited_by_count":12,"area":null},{"id":"2505.20280","title":"Lorentz Local Canonicalization: How to Make Any Network Lorentz-Equivariant","created":"2025-05-26","cited_by_count":12,"area":null},{"id":"2202.02164","title":"Group invariant machine learning by fundamental domain projections","created":"2022-02-04","cited_by_count":11,"area":null},{"id":"2203.01216","title":"A Simple and Universal Rotation Equivariant Point-cloud Network","created":"2022-03-02","cited_by_count":11,"area":null},{"id":"2301.08893","title":"Spatial Attention Kinetic Networks with E(n)-Equivariance","created":"2023-01-21","cited_by_count":11,"area":null},{"id":"2303.02216","title":"Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials","created":"2023-03-03","cited_by_count":11,"area":null},{"id":"2305.18951","title":"Subequivariant Graph Reinforcement Learning in 3D Environments","created":"2023-05-30","cited_by_count":11,"area":null},{"id":"2402.04836","title":"On the Completeness of Invariant Geometric Deep Learning Models","created":"2024-02-07","cited_by_count":11,"area":null},{"id":"2301.13724","title":"Towards fully covariant machine learning","created":"2023-01-31","cited_by_count":10,"area":null},{"id":"2301.13821","title":"Complete Neural Networks for Complete Euclidean Graphs","created":"2023-01-31","cited_by_count":10,"area":null},{"id":"2402.02484","title":"Weisfeiler Leman for Euclidean Equivariant Machine Learning","created":"2024-02-04","cited_by_count":10,"area":null},{"id":"2306.06327","title":"Any-dimensional equivariant neural networks","created":"2023-06-10","cited_by_count":9,"area":null},{"id":"2305.17017","title":"Investigating how ReLU-networks encode symmetries","created":"2023-05-26","cited_by_count":8,"area":null},{"id":"2312.16160","title":"SymmPI: Predictive Inference for Data with Group Symmetries","created":"2023-12-26","cited_by_count":8,"area":null},{"id":"2405.15932","title":"Steerable Transformers for Volumetric Data","created":"2024-05-24","cited_by_count":8,"area":null},{"id":"2408.06321","title":"EqNIO: Subequivariant Neural Inertial Odometry","created":"2024-08-12","cited_by_count":8,"area":null},{"id":"2302.14770","title":"Completeness of Atomic Structure Representations","created":"2023-02-28","cited_by_count":7,"area":null},{"id":"2303.06208","title":"Fast computation of permutation equivariant layers with the partition algebra","created":"2023-03-10","cited_by_count":7,"area":null},{"id":"2407.02025","title":"On the Expressive Power of Sparse Geometric MPNNs","created":"2024-07-02","cited_by_count":7,"area":null},{"id":"2110.03761","title":"A simple equivariant machine learning method for dynamics based on scalars","created":"2021-10-07","cited_by_count":6,"area":null},{"id":"2506.19482","title":"Fast and Distributed Equivariant Graph Neural Networks by Virtual Node Learning","created":"2025-06-24","cited_by_count":6,"area":null},{"id":"2206.14798","title":"Generalized Permutants and Graph GENEOs","created":"2022-06-29","cited_by_count":5,"area":null},{"id":"2303.04217","title":"AI for Science: An Emerging Agenda","created":"2023-03-07","cited_by_count":5,"area":null},{"id":"2311.03094","title":"Equivariance Is Not All You Need: Characterizing the Utility of Equivariant Graph Neural Networks for Particle Physics Tasks","created":"2023-11-06","cited_by_count":5,"area":null},{"id":"2409.19838","title":"Using pretrained graph neural networks with token mixers as geometric featurizers for conformational dynamics","created":"2024-09-30","cited_by_count":5,"area":null},{"id":"2410.18553","title":"Optimal Equivariant Architectures from the Symmetries of Matrix-Element Likelihoods","created":"2024-10-24","cited_by_count":5,"area":null},{"id":"2311.07143","title":"Learning Symmetrization for Equivariance with Orbit Distance Minimization","created":"2023-11-13","cited_by_count":4,"area":null},{"id":"2405.18095","title":"Is machine learning good or bad for the natural sciences?","created":"2024-05-28","cited_by_count":4,"area":null},{"id":"2409.01931","title":"On the design space between molecular mechanics and machine learning force fields","created":"2024-09-03","cited_by_count":4,"area":null},{"id":"2411.01641","title":"Lorentz-Equivariant Quantum Graph Neural Network for High-Energy Physics","created":"2024-11-03","cited_by_count":4,"area":null},{"id":"2412.19778","title":"Symbolic Approximations to Ricci-flat Metrics Via Extrinsic Symmetries of Calabi-Yau Hypersurfaces","created":"2024-12-27","cited_by_count":4,"area":null},{"id":"2210.06741","title":"Why self-attention is Natural for Sequence-to-Sequence Problems? A Perspective from Symmetries","created":"2022-10-13","cited_by_count":3,"area":null},{"id":"2402.15636","title":"Smooth and Sparse Latent Dynamics in Operator Learning with Jerk Regularization","created":"2024-02-23","cited_by_count":3,"area":null},{"id":"2406.01552","title":"Tensor learning with orthogonal, Lorentz, and symplectic symmetries","created":"2024-06-03","cited_by_count":3,"area":null},{"id":"2407.03125","title":"Foundations and Frontiers of Graph Learning Theory","created":"2024-07-03","cited_by_count":3,"area":null},{"id":"2410.05446","title":"Stability of sorting based embeddings","created":"2024-10-07","cited_by_count":3,"area":null},{"id":"2506.05530","title":"Spectral Graph Neural Networks are Incomplete on Graphs with a Simple Spectrum","created":"2025-06-05","cited_by_count":3,"area":null},{"id":"2509.24886","title":"Adaptive Canonicalization with Application to Invariant Anisotropic Geometric Networks","created":"2025-09-29","cited_by_count":3,"area":null},{"id":"2510.13169","title":"Universally Invariant Learning in Equivariant GNNs","created":"2025-10-15","cited_by_count":3,"area":null},{"id":"2605.23156","title":"Any-Dimensional Invariant Universality","created":"2026-05-22","cited_by_count":3,"area":null},{"id":"2212.14871","title":"Equivariant Light Field Convolution and Transformer","created":"2022-12-30","cited_by_count":2,"area":null},{"id":"2308.06271","title":"Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds","created":"2023-07-27","cited_by_count":2,"area":null},{"id":"2406.06611","title":"Building Hybrid B-Spline And Neural Network Operators","created":"2024-06-06","cited_by_count":2,"area":null},{"id":"2409.07327","title":"Current Symmetry Group Equivariant Convolution Frameworks for Representation Learning","created":"2024-09-11","cited_by_count":2,"area":null},{"id":"2410.09737","title":"Towards Stable, Globally Expressive Graph Representations with Laplacian Eigenvectors","created":"2024-10-13","cited_by_count":2,"area":null},{"id":"2502.15376","title":"Learning Chern Numbers of Topological Insulators with Gauge Equivariant Neural Networks","created":"2025-02-21","cited_by_count":2,"area":null},{"id":"2502.16664","title":"Geometric Kolmogorov-Arnold Superposition Theorem","created":"2025-02-23","cited_by_count":2,"area":null},{"id":"2505.05404","title":"Representing spherical tensors with scalar-based machine-learning models","created":"2025-05-08","cited_by_count":2,"area":null},{"id":"2505.16549","title":"Towards Coordinate- and Dimension-Agnostic Machine Learning for Partial Differential Equations","created":"2025-05-22","cited_by_count":2,"area":null},{"id":"2510.15403","title":"Geometric Mixture Models for Electrolyte Conductivity Prediction","created":"2025-10-17","cited_by_count":2,"area":null},{"id":"2210.09444","title":"A tradeoff between universality of equivariant models and learnability of symmetries","created":"2022-10-17","cited_by_count":1,"area":null},{"id":"2302.04545","title":"Lorentz Equivariant Model for Knowledge-Enhanced Hyperbolic Collaborative Filtering","created":"2023-02-09","cited_by_count":1,"area":null},{"id":"2402.15413","title":"G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups","created":"2024-02-23","cited_by_count":1,"area":null},{"id":"2408.04765","title":"Scalable learning of potentials to predict time-dependent Hartree-Fock dynamics","created":"2024-08-08","cited_by_count":1,"area":null},{"id":"2408.08823","title":"Optimal Symmetries in Binary Classification","created":"2024-08-16","cited_by_count":1,"area":null},{"id":"2409.13063","title":"Improved Image Classification with Manifold Neural Networks","created":"2024-09-19","cited_by_count":1,"area":null},{"id":"2409.15600","title":"Polyatomic Complexes: A topologically-informed learning representation for atomistic systems","created":"2024-09-23","cited_by_count":1,"area":null},{"id":"2505.06920","title":"Bi-directional Self-Registration for Misaligned Infrared-Visible Image Fusion","created":"2025-05-11","cited_by_count":1,"area":null},{"id":"2505.16035","title":"Equivariant Eikonal Neural Networks: Grid-Free, Scalable Travel-Time Prediction on Homogeneous Spaces","created":"2025-05-21","cited_by_count":1,"area":null},{"id":"2602.04323","title":"Efficient Equivariant High-Order Crystal Tensor Prediction via Cartesian Local-Environment Many-Body Coupling","created":"2026-02-04","cited_by_count":1,"area":null},{"id":"2602.20370","title":"Quantitative Approximation Rates for Group Equivariant Learning","created":"2026-02-23","cited_by_count":1,"area":null}],"next_cursor":"100"}