{"total":168,"rows":[{"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,"area":null},{"id":"2106.08903","title":"GemNet: Universal Directional Graph Neural Networks for Molecules","created":"2021-06-02","cited_by_count":343,"area":null},{"id":"2104.12229","title":"Vector Neurons: A General Framework for SO(3)-Equivariant Networks","created":"2021-04-25","cited_by_count":305,"area":null},{"id":"2206.11990","title":"Equiformer: Equivariant Graph Attention Transformer for 3D Atomistic Graphs","created":"2022-06-23","cited_by_count":264,"area":null},{"id":"2204.05249","title":"Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics","created":"2022-04-11","cited_by_count":256,"area":null},{"id":"2110.02905","title":"Geometric and Physical Quantities Improve E(3) Equivariant Message Passing","created":"2021-10-06","cited_by_count":228,"area":null},{"id":"2306.12059","title":"EquiformerV2: Improved Equivariant Transformer for Scaling to Higher-Degree Representations","created":"2023-06-21","cited_by_count":204,"area":null},{"id":"2208.09481","title":"Graph neural networks for materials science and chemistry","created":"2022-08-05","cited_by_count":182,"area":null},{"id":"2207.09453","title":"e3nn: Euclidean Neural Networks","created":"2022-07-18","cited_by_count":174,"area":null},{"id":"2302.03655","title":"Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs","created":"2023-02-07","cited_by_count":117,"area":null},{"id":"2107.12375","title":"Geometric Deep Learning on Molecular Representations","created":"2021-07-26","cited_by_count":113,"area":null},{"id":"2109.07103","title":"Automatic Symmetry Discovery with Lie Algebra Convolutional Network","created":"2021-09-15","cited_by_count":83,"area":null},{"id":"2110.14811","title":"SE(3) Equivariant Graph Neural Networks with Complete Local Frames","created":"2021-10-26","cited_by_count":76,"area":null},{"id":"2106.02347","title":"SE(3)-equivariant prediction of molecular wavefunctions and electronic densities","created":"2021-06-04","cited_by_count":67,"area":null},{"id":"2105.13926","title":"Geometric Deep Learning and Equivariant Neural Networks","created":"2021-05-28","cited_by_count":65,"area":null},{"id":"2210.08566","title":"Theory for Equivariant Quantum Neural Networks","created":"2022-10-16","cited_by_count":62,"area":null},{"id":"2206.14331","title":"Spherical Channels for Modeling Atomic Interactions","created":"2022-06-29","cited_by_count":47,"area":null},{"id":"2202.10638","title":"Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations","created":"2022-02-22","cited_by_count":42,"area":null},{"id":"2204.07178","title":"Relaxing Equivariance Constraints with Non-stationary Continuous Filters","created":"2022-04-14","cited_by_count":41,"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":"2210.07980","title":"Representation Theory for Geometric Quantum Machine Learning","created":"2022-10-14","cited_by_count":40,"area":null},{"id":"2103.01710","title":"Autobahn: Automorphism-based Graph Neural Nets","created":"2021-03-02","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":"2109.09721","title":"Machine-learning hidden symmetries","created":"2021-09-20","cited_by_count":31,"area":null},{"id":"2105.14655","title":"Informing Geometric Deep Learning with Electronic Interactions to Accelerate Quantum Chemistry","created":"2021-05-31","cited_by_count":30,"area":null},{"id":"2106.00694","title":"Symmetry-via-Duality: Invariant Neural Network Densities from Parameter-Space Correlators","created":"2021-06-01","cited_by_count":29,"area":null},{"id":"2112.07611","title":"Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ans\\\"atze","created":"2021-12-14","cited_by_count":29,"area":null},{"id":"2112.00652","title":"Equivariant graph neural networks for fast electron density estimation of molecules, liquids, and solids","created":"2021-12-01","cited_by_count":25,"area":null},{"id":"2202.03990","title":"Equivariance versus Augmentation for Spherical Images","created":"2022-02-08","cited_by_count":24,"area":null},{"id":"2209.13603","title":"Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions","created":"2022-09-27","cited_by_count":24,"area":null},{"id":"2203.06153","title":"Symmetry Group Equivariant Architectures for Physics","created":"2022-03-11","cited_by_count":23,"area":null},{"id":"2408.13242","title":"Improving Equivariant Model Training via Constraint Relaxation","created":"2024-08-23","cited_by_count":23,"area":null},{"id":"2501.13986","title":"An Efficient Sparse Kernel Generator for O(3)-Equivariant Deep Networks","created":"2025-01-23","cited_by_count":22,"area":null},{"id":"2105.05400","title":"Homogeneous vector bundles and $G$-equivariant convolutional neural networks","created":"2021-05-12","cited_by_count":21,"area":null},{"id":"2205.07362","title":"What is an equivariant neural network?","created":"2022-05-15","cited_by_count":21,"area":null},{"id":"2306.05420","title":"Scaling Spherical CNNs","created":"2023-06-08","cited_by_count":21,"area":null},{"id":"2102.06942","title":"Rotation-Equivariant Deep Learning for Diffusion MRI","created":"2021-02-13","cited_by_count":20,"area":null},{"id":"2102.08380","title":"Topological Obstructions to Autoencoding","created":"2021-02-16","cited_by_count":20,"area":null},{"id":"2206.08362","title":"Unified Fourier-based Kernel and Nonlinearity Design for Equivariant Networks on Homogeneous Spaces","created":"2022-06-16","cited_by_count":20,"area":null},{"id":"2307.07313","title":"HEAL-SWIN: A Vision Transformer On The Sphere","created":"2023-07-14","cited_by_count":20,"area":null},{"id":"2407.09468","title":"Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures","created":"2024-07-12","cited_by_count":20,"area":null},{"id":"2104.03584","title":"PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs","created":"2021-04-08","cited_by_count":19,"area":null},{"id":"2110.06084","title":"Implicit Bias of Linear Equivariant Networks","created":"2021-10-12","cited_by_count":19,"area":null},{"id":"2212.08892","title":"Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis","created":"2022-12-17","cited_by_count":19,"area":null},{"id":"2102.02828","title":"Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs","created":"2021-02-04","cited_by_count":18,"area":null},{"id":"2204.03640","title":"Equivariance Discovery by Learned Parameter-Sharing","created":"2022-04-07","cited_by_count":18,"area":null},{"id":"2205.13599","title":"VectorAdam for Rotation Equivariant Geometry Optimization","created":"2022-05-26","cited_by_count":18,"area":null},{"id":"2210.08001","title":"Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks","created":"2022-10-14","cited_by_count":18,"area":null},{"id":"2403.09549","title":"Generalizing Denoising to Non-Equilibrium Structures Improves Equivariant Force Fields","created":"2024-03-14","cited_by_count":18,"area":null},{"id":"2212.08630","title":"Brauer's Group Equivariant Neural Networks","created":"2022-12-16","cited_by_count":17,"area":null},{"id":"2106.06662","title":"Equivariant Networks for Pixelized Spheres","created":"2021-06-12","cited_by_count":16,"area":null},{"id":"2305.16316","title":"Making Vision Transformers Truly Shift-Equivariant","created":"2023-05-25","cited_by_count":16,"area":null},{"id":"2308.10436","title":"Approximately Equivariant Graph Networks","created":"2023-08-21","cited_by_count":16,"area":null},{"id":"2311.02922","title":"Truly Scale-Equivariant Deep Nets with Fourier Layers","created":"2023-11-06","cited_by_count":16,"area":null},{"id":"2106.05886","title":"Group Equivariant Subsampling","created":"2021-06-10","cited_by_count":15,"area":null},{"id":"2212.07347","title":"Lorentz group equivariant autoencoders","created":"2022-12-14","cited_by_count":15,"area":null},{"id":"2506.13523","title":"The Price of Freedom: Exploring Expressivity and Runtime Tradeoffs in Equivariant Tensor Products","created":"2025-06-16","cited_by_count":15,"area":null},{"id":"2209.14991","title":"Machine learning and invariant theory","created":"2022-09-29","cited_by_count":13,"area":null},{"id":"2109.07573","title":"Differentiable Physics: A Position Piece","created":"2021-09-14","cited_by_count":12,"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":"2106.15301","title":"VolterraNet: A higher order convolutional network with group equivariance for homogeneous manifolds","created":"2021-06-05","cited_by_count":11,"area":null},{"id":"2109.13901","title":"Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning","created":"2021-09-28","cited_by_count":11,"area":null},{"id":"2205.14819","title":"Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups","created":"2022-05-30","cited_by_count":11,"area":null},{"id":"2206.01909","title":"Toward Learning Robust and Invariant Representations with Alignment Regularization and Data Augmentation","created":"2022-06-04","cited_by_count":11,"area":null},{"id":"2211.03198","title":"Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation","created":"2022-11-06","cited_by_count":11,"area":null},{"id":"2604.09130","title":"EquiformerV3: Scaling Efficient, Expressive, and General SE(3)-Equivariant Graph Attention Transformers","created":"2026-04-10","cited_by_count":11,"area":null},{"id":"2302.05823","title":"Data efficiency and extrapolation trends in neural network interatomic potentials","created":"2023-02-12","cited_by_count":10,"area":null},{"id":"2505.13631","title":"Learning (Approximately) Equivariant Networks via Constrained Optimization","created":"2025-05-19","cited_by_count":10,"area":null},{"id":"2305.05666","title":"Policy Gradient Methods in the Presence of Symmetries and State Abstractions","created":"2023-05-09","cited_by_count":9,"area":null},{"id":"2306.06327","title":"Any-dimensional equivariant neural networks","created":"2023-06-10","cited_by_count":9,"area":null},{"id":"2104.09403","title":"OmniLayout: Room Layout Reconstruction from Indoor Spherical Panoramas","created":"2021-04-19","cited_by_count":8,"area":null},{"id":"2311.11106","title":"ShapeMatcher: Self-Supervised Joint Shape Canonicalization, Segmentation, Retrieval and Deformation","created":"2023-11-18","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":"2407.06053","title":"Learning local equivariant representations for quantum operators","created":"2024-07-08","cited_by_count":8,"area":null},{"id":"2502.05169","title":"Flopping for FLOPs: Leveraging equivariance for computational efficiency","created":"2025-02-07","cited_by_count":8,"area":null},{"id":"2506.02269","title":"A Tale of Two Symmetries: Exploring the Loss Landscape of Equivariant Models","created":"2025-06-02","cited_by_count":8,"area":null},{"id":"2111.10978","title":"Deformation Robust Roto-Scale-Translation Equivariant CNNs","created":"2021-11-22","cited_by_count":7,"area":null},{"id":"2202.03967","title":"Improving the Sample-Complexity of Deep Classification Networks with Invariant Integration","created":"2022-02-08","cited_by_count":7,"area":null},{"id":"2305.18983","title":"SO(2)-Equivariant Downwash Models for Close Proximity Flight","created":"2023-05-30","cited_by_count":7,"area":null},{"id":"2309.02898","title":"A Unified Framework for Discovering Discrete Symmetries","created":"2023-09-06","cited_by_count":7,"area":null},{"id":"2409.11772","title":"Symmetry-Based Structured Matrices for Efficient Approximately Equivariant Networks","created":"2024-09-18","cited_by_count":7,"area":null},{"id":"2104.11244","title":"Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering Transforms","created":"2021-04-22","cited_by_count":6,"area":null},{"id":"2206.10385","title":"Approximate Equivariance SO(3) Needlet Convolution","created":"2022-06-17","cited_by_count":6,"area":null},{"id":"2311.09312","title":"H-Packer: Holographic Rotationally Equivariant Convolutional Neural Network for Protein Side-Chain Packing","created":"2023-11-15","cited_by_count":6,"area":null},{"id":"2311.14670","title":"Differentiable and accelerated spherical harmonic and Wigner transforms","created":"2023-11-24","cited_by_count":6,"area":null},{"id":"2412.18263","title":"High-Rank Irreducible Cartesian Tensor Decomposition and Bases of Equivariant Spaces","created":"2024-12-24","cited_by_count":6,"area":null},{"id":"2109.11330","title":"Quantum algorithms for group convolution, cross-correlation, and equivariant transformations","created":"2021-09-23","cited_by_count":5,"area":null},{"id":"2206.08712","title":"An Algorithm for the SE(3)-Transformation on Neural Implicit Maps for Remapping Functions","created":"2022-06-17","cited_by_count":5,"area":null},{"id":"2301.10152","title":"How Jellyfish Characterise Alternating Group Equivariant Neural Networks","created":"2023-01-24","cited_by_count":5,"area":null},{"id":"2405.17541","title":"Approximately-symmetric neural networks for quantum spin liquids","created":"2024-05-27","cited_by_count":5,"area":null},{"id":"2207.07250","title":"Toward Super-polynomial Quantum Speedup of Equivariant Quantum Algorithms with SU($d$) Symmetry","created":"2022-07-15","cited_by_count":4,"area":null},{"id":"2208.03720","title":"PDO-s3DCNNs: Partial Differential Operator Based Steerable 3D CNNs","created":"2022-08-07","cited_by_count":4,"area":null},{"id":"2211.02936","title":"Learning the shape of protein micro-environments with a holographic convolutional neural network","created":"2022-11-05","cited_by_count":4,"area":null},{"id":"2307.07810","title":"Graph Automorphism Group Equivariant Neural Networks","created":"2023-07-15","cited_by_count":4,"area":null},{"id":"2401.16914","title":"Energy-conserving equivariant GNN for elasticity of lattice architected metamaterials","created":"2024-01-30","cited_by_count":4,"area":null},{"id":"2411.00543","title":"3D Equivariant Pose Regression via Direct Wigner-D Harmonics Prediction","created":"2024-11-01","cited_by_count":4,"area":null},{"id":"2501.07773","title":"Symmetry-Aware Generative Modeling through Learned Canonicalization","created":"2025-01-14","cited_by_count":4,"area":null},{"id":"2502.08209","title":"Equivariant Masked Position Prediction for Efficient Molecular Representation","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2106.13863","title":"Steerable 3D Spherical Neurons","created":"2021-06-02","cited_by_count":3,"area":null},{"id":"2209.10944","title":"Learning Invariant Representations for Equivariant Neural Networks Using Orthogonal Moments","created":"2022-09-22","cited_by_count":3,"area":null}],"next_cursor":"100"}