{"total":75,"rows":[{"id":"2201.11969","title":"Approximately Equivariant Networks for Imperfectly Symmetric Dynamics","created":"2022-01-28","cited_by_count":97,"area":null},{"id":"2304.05366","title":"The No Free Lunch Theorem, Kolmogorov Complexity, and the Role of Inductive Biases in Machine Learning","created":"2023-04-11","cited_by_count":63,"area":null},{"id":"2402.00809","title":"Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI","created":"2024-02-01","cited_by_count":62,"area":null},{"id":"2503.02113","title":"Deep Learning is Not So Mysterious or Different","created":"2025-03-03","cited_by_count":56,"area":null},{"id":"2305.17592","title":"Approximation-Generalization Trade-offs under (Approximate) Group Equivariance","created":"2023-05-27","cited_by_count":46,"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":"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,"area":null},{"id":"2210.04345","title":"LieGG: Studying Learned Lie Group Generators","created":"2022-10-09","cited_by_count":31,"area":null},{"id":"2206.04176","title":"VN-Transformer: Rotation-Equivariant Attention for Vector Neurons","created":"2022-06-08","cited_by_count":27,"area":null},{"id":"2203.06153","title":"Symmetry Group Equivariant Architectures for Physics","created":"2022-03-11","cited_by_count":23,"area":null},{"id":"2310.06131","title":"Learning Layer-wise Equivariances Automatically using Gradients","created":"2023-10-09","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":"2310.02299","title":"Discovering Symmetry Breaking in Physical Systems with Relaxed Group Convolution","created":"2023-10-03","cited_by_count":20,"area":null},{"id":"2206.09450","title":"Data Augmentation vs. Equivariant Networks: A Theory of Generalization on Dynamics Forecasting","created":"2022-06-19","cited_by_count":17,"area":null},{"id":"2207.09344","title":"Online Dynamics Learning for Predictive Control with an Application to Aerial Robots","created":"2022-07-19","cited_by_count":17,"area":null},{"id":"2306.00356","title":"Regularizing Towards Soft Equivariance Under Mixed Symmetries","created":"2023-06-01","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":"2402.15552","title":"Morphological Symmetries in Robotics","created":"2024-02-23","cited_by_count":13,"area":null},{"id":"2410.17878","title":"Relaxed Equivariance via Multitask Learning","created":"2024-10-23","cited_by_count":13,"area":null},{"id":"2212.00017","title":"Knowledge-augmented Deep Learning and Its Applications: A Survey","created":"2022-11-30","cited_by_count":10,"area":null},{"id":"2310.13164","title":"Almost Equivariance via Lie Algebra Convolutions","created":"2023-10-19","cited_by_count":10,"area":null},{"id":"2312.13534","title":"SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI","created":"2023-12-21","cited_by_count":10,"area":null},{"id":"2503.09829","title":"SE(3)-Equivariant Robot Learning and Control: A Tutorial Survey","created":"2025-03-12","cited_by_count":10,"area":"offlinerl"},{"id":"2505.13631","title":"Learning (Approximately) Equivariant Networks via Constrained Optimization","created":"2025-05-19","cited_by_count":10,"area":null},{"id":"2411.04225","title":"Approximate Equivariance in Reinforcement Learning","created":"2024-11-06","cited_by_count":9,"area":"deeprl"},{"id":"2210.05484","title":"Equivariance-aware Architectural Optimization of Neural Networks","created":"2022-10-11","cited_by_count":8,"area":null},{"id":"2302.10433","title":"On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis","created":"2023-02-21","cited_by_count":8,"area":null},{"id":"2406.13488","title":"Approximately Equivariant Neural Processes","created":"2024-06-19","cited_by_count":8,"area":null},{"id":"2310.17786","title":"Understanding when Dynamics-Invariant Data Augmentations Benefit Model-Free Reinforcement Learning Updates","created":"2023-10-26","cited_by_count":7,"area":null},{"id":"2311.01500","title":"E(2) Equivariant Neural Networks for Robust Galaxy Morphology Classification","created":"2023-11-02","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":"2512.11855","title":"Achieving Approximate Symmetry Is Exponentially Easier than Exact Symmetry","created":"2025-12-05","cited_by_count":7,"area":null},{"id":"2505.12083","title":"Discovering Symbolic Differential Equations with Symmetry Invariants","created":"2025-05-17","cited_by_count":6,"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":"2405.17541","title":"Approximately-symmetric neural networks for quantum spin liquids","created":"2024-05-27","cited_by_count":5,"area":null},{"id":"2412.04594","title":"Learning Symmetries via Weight-Sharing with Doubly Stochastic Tensors","created":"2024-12-05","cited_by_count":5,"area":null},{"id":"2504.10777","title":"AtlasD: Automatic Local Symmetry Discovery","created":"2025-04-15","cited_by_count":5,"area":null},{"id":"2505.13946","title":"Visual Instruction Bottleneck Tuning","created":"2025-05-20","cited_by_count":5,"area":"mllm"},{"id":"2505.19799","title":"A Regularization-Guided Equivariant Approach for Image Restoration","created":"2025-05-26","cited_by_count":5,"area":null},{"id":"2312.09323","title":"Perspectives on the State and Future of Deep Learning - 2023","created":"2023-12-07","cited_by_count":4,"area":null},{"id":"2312.12223","title":"Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries","created":"2023-12-19","cited_by_count":4,"area":null},{"id":"2403.12856","title":"Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path Planning","created":"2024-03-19","cited_by_count":4,"area":"deeprl"},{"id":"2410.08087","title":"Noether's razor: Learning Conserved Quantities","created":"2024-10-10","cited_by_count":4,"area":null},{"id":"2606.24418","title":"Data Augmentation: A Fourier Analysis Perspective","created":"2026-06-23","cited_by_count":4,"area":null},{"id":"2510.15814","title":"On Universality of Deep Equivariant Networks","created":"2025-10-17","cited_by_count":3,"area":null},{"id":"2512.00915","title":"Partially Equivariant Reinforcement Learning in Symmetry-Breaking Environments","created":"2025-11-30","cited_by_count":3,"area":"deeprl"},{"id":"2512.10723","title":"Generalized Spherical Neural Operators: Green's Function Formulation","created":"2025-12-11","cited_by_count":3,"area":null},{"id":"2309.16672","title":"Learning to Transform for Generalizable Instance-wise Invariance","created":"2023-09-28","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":"2503.15615","title":"PEnGUiN: Partially Equivariant Graph NeUral Networks for Sample Efficient MARL","created":"2025-03-19","cited_by_count":2,"area":null},{"id":"2601.05028","title":"Approximate Equivariance via Projection-based Regularisation","created":"2026-01-08","cited_by_count":2,"area":null},{"id":"2603.19486","title":"Any-Subgroup Equivariant Networks via Symmetry Breaking","created":"2026-03-19","cited_by_count":2,"area":null},{"id":"2605.08262","title":"SLayerGen: a Crystal Generative Model for all Space and Layer Groups","created":"2026-05-07","cited_by_count":2,"area":null},{"id":"2308.13357","title":"A topological model for partial equivariance in deep learning and data analysis","created":"2023-08-25","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":"2407.04271","title":"Variational Partial Group Convolutions for Input-Aware Partial Equivariance of Rotations and Color-Shifts","created":"2024-07-05","cited_by_count":1,"area":null},{"id":"2505.13289","title":"RECON: Robust symmetry discovery via Explicit Canonical Orientation Normalization","created":"2025-05-19","cited_by_count":1,"area":null},{"id":"2506.01529","title":"Learning Abstract World Models with a Group-Structured Latent Space","created":"2025-06-02","cited_by_count":1,"area":"wm"},{"id":"2506.18629","title":"On Equivariant Model Selection through the Lens of Uncertainty","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2512.11345","title":"Symmetry-Aware Steering of Equivariant Diffusion Policies: Benefits and Limits","created":"2025-12-12","cited_by_count":1,"area":"diffusion"},{"id":"2512.14980","title":"Softly Constrained Denoisers for Diffusion Models Applied to Partial Differential Equations","created":"2025-12-17","cited_by_count":1,"area":"diffusion"},{"id":"2603.06693","title":"Soft Equivariance Regularization for Invariant Self-Supervised Learning","created":"2026-03-04","cited_by_count":1,"area":null},{"id":"2609.08133","title":"Sparse Data Augmentation for Optimization with Provable Guarantees","created":"2026-09-08","cited_by_count":1,"area":null},{"id":"2506.08244","title":"Algebraic Priors for Approximately Equivariant Networks","created":"2025-06-09","cited_by_count":0,"area":null},{"id":"2511.04243","title":"Twirlator: A Pipeline for Analyzing Subgroup Symmetry Effects in Quantum Machine Learning Ansatzes","created":"2025-11-06","cited_by_count":0,"area":null},{"id":"2512.15493","title":"Soft Geometric Inductive Bias for Object Centric Dynamics","created":"2025-12-17","cited_by_count":0,"area":null},{"id":"2601.14000","title":"Group-Invariant Unsupervised Skill Discovery: Symmetry-aware Skill Representations for Generalizable Behavior","created":"2026-01-20","cited_by_count":0,"area":null},{"id":"2601.20172","title":"Loss Landscape Geometry of Partial Differential Equation Emulators: Or, Symmetry Learning via Gradient Alignment","created":"2026-01-28","cited_by_count":0,"area":null},{"id":"2602.02853","title":"Recurrent Equivariant Constraint Modulation: Learning Per-Layer Symmetry Relaxation from Data","created":"2026-02-02","cited_by_count":0,"area":null},{"id":"2603.26657","title":"Tunable Soft Equivariance with Guarantees","created":"2026-03-27","cited_by_count":0,"area":null},{"id":"2606.16900","title":"Factorized Neural Operators Decompose Dynamic and Persistent Responses","created":"2026-06-15","cited_by_count":0,"area":null},{"id":"2607.28755","title":"TAGTorch: A PyTorch Library for Geometry, Topology, and Symmetry-Aware Machine Learning","created":"2026-07-30","cited_by_count":0,"area":null},{"id":"2608.11318","title":"Terminal Symmetry as a Carrier of Asymmetric Process Knowledge: Statewise Refinement for Anytime Verified Construction","created":"2026-08-11","cited_by_count":0,"area":null},{"id":"2609.08381","title":"Equivariance Breaks the Learning Rate","created":"2026-09-08","cited_by_count":0,"area":null},{"id":"2610.00157","title":"Symmetry Discovery in Quantum Learning: Observable-Level and Task-Level Inference from Finite Measurements","created":"2026-09-11","cited_by_count":0,"area":null}],"next_cursor":null}