{"total":96,"rows":[{"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":"2207.02505","title":"Pure Transformers are Powerful Graph Learners","created":"2022-07-06","cited_by_count":188,"area":null},{"id":"2104.09459","title":"A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups","created":"2021-04-19","cited_by_count":187,"area":null},{"id":"2109.00179","title":"Spatio-temporal Self-Supervised Representation Learning for 3D Point Clouds","created":"2021-09-01","cited_by_count":180,"area":null},{"id":"2110.02910","title":"Equivariant Subgraph Aggregation Networks","created":"2021-10-06","cited_by_count":154,"area":null},{"id":"2106.06610","title":"Scalars are universal: Equivariant machine learning, structured like classical physics","created":"2021-06-11","cited_by_count":127,"area":null},{"id":"2112.09992","title":"Weisfeiler and Leman go Machine Learning: The Story so far","created":"2021-12-18","cited_by_count":122,"area":null},{"id":"2206.11140","title":"Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries","created":"2022-06-22","cited_by_count":112,"area":null},{"id":"2301.12780","title":"Equivariant Architectures for Learning in Deep Weight Spaces","created":"2023-01-30","cited_by_count":107,"area":null},{"id":"2201.11969","title":"Approximately Equivariant Networks for Imperfectly Symmetric Dynamics","created":"2022-01-28","cited_by_count":97,"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":"2112.01388","title":"Residual Pathway Priors for Soft Equivariance Constraints","created":"2021-12-02","cited_by_count":75,"area":null},{"id":"2302.14040","title":"Permutation Equivariant Neural Functionals","created":"2023-02-27","cited_by_count":70,"area":null},{"id":"2206.11168","title":"Ordered Subgraph Aggregation Networks","created":"2022-06-22","cited_by_count":57,"area":null},{"id":"2210.09974","title":"Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks","created":"2022-10-18","cited_by_count":52,"area":null},{"id":"2305.13546","title":"Neural Functional Transformers","created":"2023-05-22","cited_by_count":52,"area":null},{"id":"2310.13397","title":"Equivariant Deep Weight Space Alignment","created":"2023-10-20","cited_by_count":35,"area":null},{"id":"2211.09231","title":"The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry","created":"2022-11-16","cited_by_count":33,"area":null},{"id":"2303.02918","title":"Graph Positional Encoding via Random Feature Propagation","created":"2023-03-06","cited_by_count":31,"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":"2303.14269","title":"The Exact Sample Complexity Gain from Invariances for Kernel Regression","created":"2023-03-24","cited_by_count":28,"area":null},{"id":"2406.06248","title":"Compute Better Spent: Replacing Dense Layers with Structured Matrices","created":"2024-06-10","cited_by_count":28,"area":null},{"id":"2306.02866","title":"Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance","created":"2023-06-05","cited_by_count":26,"area":null},{"id":"2211.15420","title":"Equivariant Networks for Crystal Structures","created":"2022-11-15","cited_by_count":24,"area":null},{"id":"2307.06046","title":"A Multi-Task Perspective for Link Prediction with New Relation Types and Nodes","created":"2023-07-12","cited_by_count":24,"area":null},{"id":"2502.19512","title":"TRIX: A More Expressive Model for Zero-shot Domain Transfer in Knowledge Graphs","created":"2025-02-26","cited_by_count":24,"area":null},{"id":"2209.00517","title":"The Neural Process Family: Survey, Applications and Perspectives","created":"2022-09-01","cited_by_count":23,"area":null},{"id":"2111.04927","title":"Self-Interpretable Model with TransformationEquivariant Interpretation","created":"2021-11-09","cited_by_count":21,"area":null},{"id":"2201.12176","title":"Generative Coarse-Graining of Molecular Conformations","created":"2022-01-28","cited_by_count":21,"area":null},{"id":"2302.01313","title":"Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types","created":"2023-02-02","cited_by_count":21,"area":null},{"id":"2106.00967","title":"Multiresolution Equivariant Graph Variational Autoencoder","created":"2021-06-02","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":"2305.14749","title":"gRNAde: Geometric Deep Learning for 3D RNA inverse design","created":"2023-05-24","cited_by_count":20,"area":null},{"id":"2110.09140","title":"Learning Prototype-oriented Set Representations for Meta-Learning","created":"2021-10-18","cited_by_count":19,"area":null},{"id":"2303.04745","title":"A General Theory of Correct, Incorrect, and Extrinsic Equivariance","created":"2023-03-08","cited_by_count":19,"area":null},{"id":"2204.03640","title":"Equivariance Discovery by Learned Parameter-Sharing","created":"2022-04-07","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":"2207.03116","title":"Equivariant Representation Learning via Class-Pose Decomposition","created":"2022-07-07","cited_by_count":17,"area":null},{"id":"2506.14291","title":"Equivariance Everywhere All At Once: A Recipe for Graph Foundation Models","created":"2025-06-17","cited_by_count":17,"area":null},{"id":"2305.16316","title":"Making Vision Transformers Truly Shift-Equivariant","created":"2023-05-25","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":"2104.06703","title":"Deep Permutation Equivariant Structure from Motion","created":"2021-04-14","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":"2406.04336","title":"On the Expressive Power of Spectral Invariant Graph Neural Networks","created":"2024-06-06","cited_by_count":15,"area":null},{"id":"2208.10428","title":"Equivariant Hypergraph Neural Networks","created":"2022-08-22","cited_by_count":14,"area":null},{"id":"2404.07097","title":"Fast Encoder-Based 3D from Casual Videos via Point Track Processing","created":"2024-04-10","cited_by_count":14,"area":null},{"id":"2407.11083","title":"Empowering Graph Invariance Learning with Deep Spurious Infomax","created":"2024-07-13","cited_by_count":14,"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":"2106.08551","title":"Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks","created":"2021-06-16","cited_by_count":10,"area":null},{"id":"2401.09235","title":"A Characterization Theorem for Equivariant Networks with Point-wise Activations","created":"2024-01-17","cited_by_count":9,"area":null},{"id":"2406.19244","title":"Improving the Expressiveness of $K$-hop Message-Passing GNNs by Injecting Contextualized Substructure Information","created":"2024-06-27","cited_by_count":9,"area":null},{"id":"2504.02544","title":"Fourier Sliced-Wasserstein Embedding for Multisets and Measures","created":"2025-04-03","cited_by_count":8,"area":null},{"id":"2204.03065","title":"The Self-Optimal-Transport Feature Transform","created":"2022-04-06","cited_by_count":7,"area":null},{"id":"2303.00728","title":"On the universality of $S_n$-equivariant $k$-body gates","created":"2023-03-01","cited_by_count":7,"area":null},{"id":"2507.01649","title":"GradMetaNet: An Equivariant Architecture for Learning on Gradients","created":"2025-07-02","cited_by_count":7,"area":null},{"id":"2107.07508","title":"USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems","created":"2021-07-15","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":"2110.08092","title":"Equivariant and Invariant Reynolds Networks","created":"2021-10-15","cited_by_count":4,"area":null},{"id":"2308.00273","title":"Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions","created":"2023-08-01","cited_by_count":4,"area":null},{"id":"2402.08529","title":"Approximately Piecewise E(3) Equivariant Point Networks","created":"2024-02-13","cited_by_count":4,"area":null},{"id":"2506.02293","title":"On Universality Classes of Equivariant Networks","created":"2025-06-02","cited_by_count":4,"area":null},{"id":"2211.07482","title":"Unifying O(3) Equivariant Neural Networks Design with Tensor-Network Formalism","created":"2022-11-14","cited_by_count":3,"area":null},{"id":"2304.07152","title":"Combining Stochastic Explainers and Subgraph Neural Networks can Increase Expressivity and Interpretability","created":"2023-04-14","cited_by_count":3,"area":null},{"id":"2501.14991","title":"Advances in Set Function Learning: A Survey of Techniques and Applications","created":"2025-01-24","cited_by_count":3,"area":null},{"id":"2507.21938","title":"Multi-state Protein Design with DynamicMPNN","created":"2025-07-29","cited_by_count":3,"area":null},{"id":"2102.12232","title":"Abelian Neural Networks","created":"2021-02-24","cited_by_count":2,"area":null},{"id":"2110.07472","title":"Capacity of Group-invariant Linear Readouts from Equivariant Representations: How Many Objects can be Linearly Classified Under All Possible Views?","created":"2021-10-14","cited_by_count":2,"area":null},{"id":"2111.01892","title":"Equivariant Deep Dynamical Model for Motion Prediction","created":"2021-11-02","cited_by_count":2,"area":null},{"id":"2406.08966","title":"Separation Power of Equivariant Neural Networks","created":"2024-06-13","cited_by_count":2,"area":null},{"id":"2407.01467","title":"The Balanced-Pairwise-Affinities Feature Transform","created":"2024-06-25","cited_by_count":2,"area":null},{"id":"2410.06665","title":"Revisiting Multi-Permutation Equivariance through the Lens of Irreducible Representations","created":"2024-10-09","cited_by_count":2,"area":null},{"id":"2502.02479","title":"Using Random Noise Equivariantly to Boost Graph Neural Networks Universally","created":"2025-02-04","cited_by_count":2,"area":null},{"id":"2506.12231","title":"CheMixHub: Datasets and Benchmarks for Chemical Mixture Property Prediction","created":"2025-06-13","cited_by_count":2,"area":null},{"id":"2602.16316","title":"A Graph Meta-Network for Learning on Kolmogorov-Arnold Networks","created":"2026-02-18","cited_by_count":2,"area":null},{"id":"2106.12891","title":"Encoding Involutory Invariances in Neural Networks","created":"2021-06-07","cited_by_count":1,"area":null},{"id":"2107.12480","title":"Circular-Symmetric Correlation Layer based on FFT","created":"2021-07-26","cited_by_count":1,"area":null},{"id":"2111.08356","title":"On-Demand Unlabeled Personalized Federated Learning","created":"2021-11-16","cited_by_count":1,"area":null},{"id":"2210.15182","title":"Text2Model: Text-based Model Induction for Zero-shot Image Classification","created":"2022-10-27","cited_by_count":1,"area":null},{"id":"2310.09675","title":"Efficient Model-Agnostic Multi-Group Equivariant Networks","created":"2023-10-14","cited_by_count":1,"area":null},{"id":"2402.03139","title":"Enhancing Neural Subset Selection: Integrating Background Information into Set Representations","created":"2024-02-05","cited_by_count":1,"area":null},{"id":"2510.21691","title":"On Uncertainty Calibration for Equivariant Functions","created":"2025-10-24","cited_by_count":1,"area":null},{"id":"2105.14117","title":"About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations","created":"2021-05-28","cited_by_count":0,"area":null},{"id":"2201.00732","title":"Classifying Turbulent Environments via Machine Learning","created":"2022-01-03","cited_by_count":0,"area":null},{"id":"2311.06358","title":"Compact Matrix Quantum Group Equivariant Neural Networks","created":"2023-11-10","cited_by_count":0,"area":null},{"id":"2405.16519","title":"Fourier Sliced-Wasserstein Embedding for Multisets and Measures","created":"2024-05-26","cited_by_count":0,"area":null},{"id":"2409.16922","title":"Decomposition of Equivariant Maps via Invariant Maps: Application to Universal Approximation under Symmetry","created":"2024-09-25","cited_by_count":0,"area":null},{"id":"2503.11276","title":"Permutation Equivariant Neural Networks for Symmetric Tensors","created":"2025-03-14","cited_by_count":0,"area":null},{"id":"2503.12978","title":"Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach","created":"2025-03-17","cited_by_count":0,"area":null},{"id":"2509.22889","title":"Convolutional Set Transformer","created":"2025-09-26","cited_by_count":0,"area":null},{"id":"2509.24472","title":"FS-KAN: Permutation Equivariant Kolmogorov-Arnold Networks via Function Sharing","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2512.15469","title":"Metanetworks as Regulatory Operators: Learning to Edit for Requirement Compliance","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2603.03511","title":"Orbital Transformers for Predicting Wavefunctions in Time-Dependent Density Functional Theory","created":"2026-03-03","cited_by_count":0,"area":null},{"id":"2607.11555","title":"Advancing Optimal Subset Oracle via Learning Relaxation of Neural Set Functions","created":"2026-07-13","cited_by_count":0,"area":null},{"id":"2610.18471","title":"Graph Metanetworks for Transformer Based Language Models","created":"2026-10-13","cited_by_count":0,"area":null}],"next_cursor":null}