{"total":34,"rows":[{"id":"2408.10205","title":"KAN 2.0: Kolmogorov-Arnold Networks Meet Science","created":"2024-08-19","cited_by_count":118,"area":null},{"id":"2302.00236","title":"Generative Adversarial Symmetry Discovery","created":"2023-02-01","cited_by_count":42,"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":"2101.06317","title":"Machine-Learning Mathematical Structures","created":"2021-01-15","cited_by_count":25,"area":null},{"id":"2108.01403","title":"Nonperturbative renormalization for the neural network-QFT correspondence","created":"2021-08-03","cited_by_count":24,"area":null},{"id":"2301.05638","title":"Deep Learning Symmetries and Their Lie Groups, Algebras, and Subalgebras from First Principles","created":"2023-01-13","cited_by_count":22,"area":null},{"id":"2312.09597","title":"Deep Generative Models for Detector Signature Simulation: A Taxonomic Review","created":"2023-12-15","cited_by_count":18,"area":null},{"id":"2306.00356","title":"Regularizing Towards Soft Equivariance Under Mixed Symmetries","created":"2023-06-01","cited_by_count":16,"area":null},{"id":"2112.06350","title":"Machine Learning Calabi-Yau Hypersurfaces","created":"2021-12-12","cited_by_count":15,"area":null},{"id":"2503.23616","title":"Interpretable Machine Learning in Physics: A Review","created":"2025-03-30","cited_by_count":14,"area":null},{"id":"2103.06115","title":"Symmetry meets AI","created":"2021-03-10","cited_by_count":13,"area":null},{"id":"2108.02221","title":"Deep multi-task mining Calabi-Yau four-folds","created":"2021-08-04","cited_by_count":11,"area":null},{"id":"2203.13847","title":"Cluster Algebras: Network Science and Machine Learning","created":"2022-03-25","cited_by_count":9,"area":null},{"id":"2302.05383","title":"Discovering Sparse Representations of Lie Groups with Machine Learning","created":"2023-02-10","cited_by_count":8,"area":null},{"id":"2410.09841","title":"Symmetry Discovery for Different Data Types","created":"2024-10-13","cited_by_count":8,"area":null},{"id":"2307.04891","title":"Accelerated Discovery of Machine-Learned Symmetries: Deriving the Exceptional Lie Groups G2, F4 and E6","created":"2023-07-10","cited_by_count":6,"area":null},{"id":"2309.07860","title":"Identifying the Group-Theoretic Structure of Machine-Learned Symmetries","created":"2023-09-14","cited_by_count":6,"area":null},{"id":"2302.00806","title":"Oracle-Preserving Latent Flows","created":"2023-02-02","cited_by_count":5,"area":null},{"id":"2405.19973","title":"A Triumvirate of AI Driven Theoretical Discovery","created":"2024-05-30","cited_by_count":5,"area":null},{"id":"2308.00015","title":"Exploring how a Generative AI interprets music","created":"2023-07-31","cited_by_count":4,"area":null},{"id":"2202.11099","title":"Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks","created":"2022-02-22","cited_by_count":3,"area":null},{"id":"2307.15834","title":"Non-parametric Hypothesis Tests for Distributional Group Symmetry","created":"2023-07-28","cited_by_count":3,"area":null},{"id":"2202.06086","title":"From the String Landscape to the Mathematical Landscape: a Machine-Learning Outlook","created":"2022-02-12","cited_by_count":2,"area":null},{"id":"2503.03014","title":"Learning finite symmetry groups of dynamical systems via equivariance detection","created":"2025-03-04","cited_by_count":2,"area":null},{"id":"2201.01644","title":"Machine-Learning the Classification of Spacetimes","created":"2022-01-05","cited_by_count":1,"area":null},{"id":"2206.11970","title":"Learning quantum symmetries with interactive quantum-classical variational algorithms","created":"2022-06-23","cited_by_count":1,"area":null},{"id":"2311.11847","title":"Deep learning complete intersection Calabi-Yau manifolds","created":"2023-11-20","cited_by_count":1,"area":null},{"id":"2406.03896","title":"Data-driven discovery of self-similarity using neural networks","created":"2024-06-06","cited_by_count":1,"area":null},{"id":"2410.05232","title":"SymmetryLens: Unsupervised Symmetry Learning via Locality and Density Preservation","created":"2024-10-07","cited_by_count":1,"area":null},{"id":"2503.03809","title":"Non-Gaussianities in Collider Metric Binning","created":"2025-03-05","cited_by_count":0,"area":null},{"id":"2602.07736","title":"Global Symmetry and Orthogonal Transformations from Geometrical Moment $n$-tuples","created":"2026-02-08","cited_by_count":0,"area":null},{"id":"2609.33527","title":"Discovering Symmetries in Neural Network Parameter Spaces","created":"2026-09-27","cited_by_count":0,"area":null},{"id":"2609.33663","title":"LieDiscover: Adaptive Symbolic Library Construction for Explicit Open-form Symmetry Discovery","created":"2026-09-27","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}