{"total":142,"rows":[{"id":"2204.10532","title":"End-to-end symbolic regression with transformers","created":"2022-04-22","cited_by_count":203,"area":null},{"id":"2106.06427","title":"Neural Symbolic Regression that Scales","created":"2021-06-11","cited_by_count":192,"area":null},{"id":"2106.14131","title":"SymbolicGPT: A Generative Transformer Model for Symbolic Regression","created":"2021-06-27","cited_by_count":105,"area":null},{"id":"2211.10873","title":"Interpretable Scientific Discovery with Symbolic Regression: A Review","created":"2022-11-20","cited_by_count":105,"area":null},{"id":"2111.00053","title":"Symbolic Regression via Neural-Guided Genetic Programming Population Seeding","created":"2021-10-29","cited_by_count":101,"area":null},{"id":"2202.02306","title":"Rediscovering orbital mechanics with machine learning","created":"2022-02-04","cited_by_count":71,"area":null},{"id":"2205.13134","title":"Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search","created":"2022-05-26","cited_by_count":66,"area":null},{"id":"2205.15764","title":"SymFormer: End-to-end symbolic regression using transformer-based architecture","created":"2022-05-31","cited_by_count":59,"area":null},{"id":"2201.04600","title":"Deep Symbolic Regression for Recurrent Sequences","created":"2022-01-12","cited_by_count":54,"area":null},{"id":"2305.01582","title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl","created":"2023-05-02","cited_by_count":54,"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":"2406.02924","title":"Pruner-Zero: Evolving Symbolic Pruning Metric from scratch for Large Language Models","created":"2024-06-05","cited_by_count":40,"area":null},{"id":"2306.12964","title":"Generating Synergistic Formulaic Alpha Collections via Reinforcement Learning","created":"2023-05-25","cited_by_count":39,"area":null},{"id":"2305.13341","title":"Discovering Causal Relations and Equations from Data","created":"2023-05-21","cited_by_count":36,"area":null},{"id":"2204.12005","title":"gLaSDI: Parametric Physics-informed Greedy Latent Space Dynamics Identification","created":"2022-04-26","cited_by_count":30,"area":null},{"id":"2103.15624","title":"Shape-constrained Symbolic Regression -- Improving Extrapolation with Prior Knowledge","created":"2021-03-29","cited_by_count":27,"area":null},{"id":"2109.05364","title":"Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling","created":"2021-09-11","cited_by_count":27,"area":null},{"id":"2406.18394","title":"AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha Factors","created":"2024-06-26","cited_by_count":27,"area":null},{"id":"2307.12617","title":"Predicting Ordinary Differential Equations with Transformers","created":"2023-07-24","cited_by_count":26,"area":null},{"id":"2201.12354","title":"Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning","created":"2022-01-28","cited_by_count":24,"area":null},{"id":"2207.00529","title":"Deep Learning and Symbolic Regression for Discovering Parametric Equations","created":"2022-07-01","cited_by_count":23,"area":null},{"id":"2205.09751","title":"Taylor Genetic Programming for Symbolic Regression","created":"2022-04-28","cited_by_count":19,"area":null},{"id":"2305.02251","title":"Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems","created":"2023-05-03","cited_by_count":19,"area":null},{"id":"2105.02368","title":"Physics-informed Spline Learning for Nonlinear Dynamics Discovery","created":"2021-05-05","cited_by_count":18,"area":null},{"id":"2501.15638","title":"A Comprehensive Survey on Self-Interpretable Neural Networks","created":"2025-01-26","cited_by_count":18,"area":null},{"id":"2505.03977","title":"Call for Action: towards the next generation of symbolic regression benchmark","created":"2025-05-06","cited_by_count":17,"area":null},{"id":"2309.13705","title":"A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data","created":"2023-09-24","cited_by_count":16,"area":null},{"id":"2302.00773","title":"Toward Physically Plausible Data-Driven Models: A Novel Neural Network Approach to Symbolic Regression","created":"2023-02-01","cited_by_count":14,"area":null},{"id":"2405.07761","title":"LLM4ED: Large Language Models for Automatic Equation Discovery","created":"2024-05-13","cited_by_count":14,"area":null},{"id":"2202.10337","title":"Integration of knowledge and data in machine learning","created":"2022-02-15","cited_by_count":13,"area":null},{"id":"2206.05922","title":"From Perception to Programs: Regularize, Overparameterize, and Amortize","created":"2022-06-13","cited_by_count":13,"area":null},{"id":"2311.06380","title":"Theory and implementation of inelastic Constitutive Artificial Neural Networks","created":"2023-11-10","cited_by_count":12,"area":null},{"id":"2205.15569","title":"GSR: A Generalized Symbolic Regression Approach","created":"2022-05-31","cited_by_count":11,"area":null},{"id":"2310.05227","title":"Physics-aware Machine Learning Revolutionizes Scientific Paradigm for Machine Learning and Process-based Hydrology","created":"2023-10-08","cited_by_count":11,"area":null},{"id":"2105.06331","title":"Informed Equation Learning","created":"2021-05-13","cited_by_count":10,"area":null},{"id":"2310.05537","title":"ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization","created":"2023-10-09","cited_by_count":10,"area":null},{"id":"2311.02104","title":"Efficient Symbolic Policy Learning with Differentiable Symbolic Expression","created":"2023-11-02","cited_by_count":10,"area":null},{"id":"2504.13112","title":"Hadamard product in deep learning: Introduction, Advances and Challenges","created":"2025-04-17","cited_by_count":9,"area":null},{"id":"2102.11544","title":"Identifying Physical Law of Hamiltonian Systems via Meta-Learning","created":"2021-02-23","cited_by_count":8,"area":null},{"id":"2210.08342","title":"Symbolic Recovery of Differential Equations: The Identifiability Problem","created":"2022-10-15","cited_by_count":8,"area":null},{"id":"2305.14656","title":"RSRM: Reinforcement Symbolic Regression Machine","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2305.15174","title":"Simultaneous identification of models and parameters of scientific simulators","created":"2023-05-24","cited_by_count":8,"area":null},{"id":"2309.07672","title":"Physics-constrained robust learning of open-form partial differential equations from limited and noisy data","created":"2023-09-14","cited_by_count":8,"area":null},{"id":"2107.07345","title":"Inferring the Structure of Ordinary Differential Equations","created":"2021-07-05","cited_by_count":7,"area":null},{"id":"2205.11798","title":"Symbolic Expression Transformer: A Computer Vision Approach for Symbolic Regression","created":"2022-05-24","cited_by_count":7,"area":null},{"id":"2206.00257","title":"CoNSoLe: Convex Neural Symbolic Learning","created":"2022-06-01","cited_by_count":7,"area":null},{"id":"2209.07736","title":"Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study","created":"2022-09-16","cited_by_count":7,"area":null},{"id":"2312.10913","title":"GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations","created":"2023-12-18","cited_by_count":7,"area":null},{"id":"2401.09949","title":"SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression","created":"2024-01-18","cited_by_count":7,"area":null},{"id":"2509.20529","title":"MDBench: Benchmarking Data-Driven Methods for Model Discovery","created":"2025-09-24","cited_by_count":7,"area":null},{"id":"2106.01078","title":"Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations","created":"2021-06-02","cited_by_count":6,"area":null},{"id":"2201.01305","title":"Augmenting astrophysical scaling relations with machine learning: application to reducing the Sunyaev-Zeldovich flux-mass scatter","created":"2022-01-04","cited_by_count":6,"area":null},{"id":"2304.01117","title":"Interpretable Symbolic Regression for Data Science: Analysis of the 2022 Competition","created":"2023-04-03","cited_by_count":6,"area":null},{"id":"2306.04120","title":"MESSY Estimation: Maximum-Entropy based Stochastic and Symbolic densitY Estimation","created":"2023-06-07","cited_by_count":6,"area":null},{"id":"2505.12083","title":"Discovering Symbolic Differential Equations with Symmetry Invariants","created":"2025-05-17","cited_by_count":6,"area":null},{"id":"2511.08860","title":"When is a System Discoverable from Data? Discovery Requires Chaos","created":"2025-11-12","cited_by_count":6,"area":null},{"id":"2401.15103","title":"PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression","created":"2024-01-25","cited_by_count":5,"area":null},{"id":"2403.01723","title":"Statistical Mechanics of Dynamical System Identification","created":"2024-03-04","cited_by_count":5,"area":null},{"id":"2412.12347","title":"AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery","created":"2024-12-16","cited_by_count":5,"area":null},{"id":"2502.01702","title":"Al-Khwarizmi: Discovering Physical Laws with Foundation Models","created":"2025-02-03","cited_by_count":5,"area":null},{"id":"2301.06064","title":"Symbolic expression generation via Variational Auto-Encoder","created":"2023-01-15","cited_by_count":4,"area":null},{"id":"2304.08915","title":"Differentiable Genetic Programming for High-dimensional Symbolic Regression","created":"2023-04-18","cited_by_count":4,"area":null},{"id":"2408.14780","title":"GINN-KAN: Interpretability pipelining with applications in Physics Informed Neural Networks","created":"2024-08-27","cited_by_count":4,"area":null},{"id":"2210.02181","title":"DISCOVER: Deep identification of symbolically concise open-form PDEs via enhanced reinforcement-learning","created":"2022-10-04","cited_by_count":3,"area":null},{"id":"2211.02830","title":"Discovering ordinary differential equations that govern time-series","created":"2022-11-05","cited_by_count":3,"area":null},{"id":"2312.08472","title":"AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions","created":"2023-12-13","cited_by_count":3,"area":null},{"id":"2401.04978","title":"Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients","created":"2024-01-10","cited_by_count":3,"area":null},{"id":"2405.04161","title":"Decoding complexity: how machine learning is redefining scientific discovery","created":"2024-05-07","cited_by_count":3,"area":null},{"id":"2103.09414","title":"Toward Neural-Network-Guided Program Synthesis and Verification","created":"2021-03-17","cited_by_count":2,"area":null},{"id":"2202.04367","title":"A Reinforcement Learning Approach to Domain-Knowledge Inclusion Using Grammar Guided Symbolic Regression","created":"2022-02-09","cited_by_count":2,"area":null},{"id":"2302.04791","title":"Symbolic Metamodels for Interpreting Black-boxes Using Primitive Functions","created":"2023-02-09","cited_by_count":2,"area":null},{"id":"2302.10539","title":"Online Symbolic Regression with Informative Query","created":"2023-02-21","cited_by_count":2,"area":null},{"id":"2310.06609","title":"Discovering Interpretable Physical Models using Symbolic Regression and Discrete Exterior Calculus","created":"2023-10-10","cited_by_count":2,"area":null},{"id":"2404.15742","title":"Generalizing the SINDy approach with nested neural networks","created":"2024-04-24","cited_by_count":2,"area":null},{"id":"2405.06848","title":"ISR: Invertible Symbolic Regression","created":"2024-05-10","cited_by_count":2,"area":null},{"id":"2405.14620","title":"Closed-form Solutions: A New Perspective on Solving Differential Equations","created":"2024-05-23","cited_by_count":2,"area":null},{"id":"2406.17834","title":"Univariate Skeleton Prediction in Multivariate Systems Using Transformers","created":"2024-06-25","cited_by_count":2,"area":null},{"id":"2412.11139","title":"ViSymRe: Vision Multimodal Symbolic Regression","created":"2024-12-15","cited_by_count":2,"area":null},{"id":"2501.08086","title":"A Neural Operator-Based Approach to Symbolic Discovery of PDEs","created":"2025-01-14","cited_by_count":2,"area":null},{"id":"2501.18563","title":"No Equations Needed: Learning System Dynamics Without Relying on Closed-Form ODEs","created":"2025-01-30","cited_by_count":2,"area":null},{"id":"2503.08059","title":"Symbolic Neural Ordinary Differential Equations","created":"2025-03-11","cited_by_count":2,"area":null},{"id":"2506.08267","title":"Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression","created":"2025-06-09","cited_by_count":2,"area":null},{"id":"2508.19672","title":"$\\mathcal{C}^1$-approximation with rational functions and rational neural networks","created":"2025-08-27","cited_by_count":2,"area":null},{"id":"2508.20257","title":"Discovering equations from data: symbolic regression in dynamical systems","created":"2025-08-27","cited_by_count":2,"area":null},{"id":"2508.21484","title":"Data-driven Discovery of Digital Twins in Biomedical Research","created":"2025-08-29","cited_by_count":2,"area":null},{"id":"2511.05849","title":"EGG-SR: Embedding Symbolic Equivalence into Symbolic Regression via Equality Graph","created":"2025-11-08","cited_by_count":2,"area":null},{"id":"2512.07875","title":"Softly Symbolifying Kolmogorov-Arnold Networks","created":"2025-11-27","cited_by_count":2,"area":null},{"id":"2512.15920","title":"Introduction to Symbolic Regression in the Physical Sciences","created":"2025-12-17","cited_by_count":2,"area":null},{"id":"2602.02886","title":"Mixture of Concept Bottleneck Experts","created":"2026-02-02","cited_by_count":2,"area":null},{"id":"2604.16232","title":"Neuro-Symbolic ODE Discovery with Latent Grammar Flow","created":"2026-04-17","cited_by_count":2,"area":null},{"id":"2110.08382","title":"A Neural Network Ensemble Approach to System Identification","created":"2021-10-15","cited_by_count":1,"area":null},{"id":"2206.04791","title":"Learning Reduced Nonlinear State-Space Models: an Output-Error Based Canonical Approach","created":"2022-04-19","cited_by_count":1,"area":null},{"id":"2209.01521","title":"Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems","created":"2022-09-04","cited_by_count":1,"area":null},{"id":"2306.00153","title":"Information Fusion via Symbolic Regression: A Tutorial in the Context of Human Health","created":"2023-05-31","cited_by_count":1,"area":null},{"id":"2404.09788","title":"Shape Arithmetic Expressions: Advancing Scientific Discovery Beyond Closed-Form Equations","created":"2024-04-15","cited_by_count":1,"area":null},{"id":"2405.10563","title":"Function Extrapolation with Neural Networks and Its Application for Manifolds","created":"2024-05-17","cited_by_count":1,"area":null},{"id":"2407.00065","title":"A Personalised Learning Tool for Physics Undergraduate Students Built On a Large Language Model for Symbolic Regression","created":"2024-06-17","cited_by_count":1,"area":null},{"id":"2409.05305","title":"Closed-Form Interpretation of Neural Network Latent Spaces with Symbolic Gradients","created":"2024-09-09","cited_by_count":1,"area":null},{"id":"2410.11587","title":"Baseflow identification via explainable AI with Kolmogorov-Arnold networks","created":"2024-10-10","cited_by_count":1,"area":null},{"id":"2503.23617","title":"Graph-Eq: Discovering Mathematical Equations using Graph Generative Models","created":"2025-03-30","cited_by_count":1,"area":null}],"next_cursor":"100"}