{"total":87,"rows":[{"id":"2306.17844","title":"The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks","created":"2023-06-30","cited_by_count":156,"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":"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":"2201.04600","title":"Deep Symbolic Regression for Recurrent Sequences","created":"2022-01-12","cited_by_count":54,"area":null},{"id":"2206.10540","title":"Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery","created":"2022-06-21","cited_by_count":46,"area":null},{"id":"2305.13341","title":"Discovering Causal Relations and Equations from Data","created":"2023-05-21","cited_by_count":36,"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":"2107.10879","title":"Discovering Sparse Interpretable Dynamics from Partial Observations","created":"2021-07-22","cited_by_count":25,"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":"2104.05417","title":"An Approach to Symbolic Regression Using Feyn","created":"2021-04-12","cited_by_count":20,"area":null},{"id":"2205.09751","title":"Taylor Genetic Programming for Symbolic Regression","created":"2022-04-28","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":"2411.03753","title":"Symbolic regression via MDLformer-guided search: from minimizing prediction error to minimizing description length","created":"2024-11-06","cited_by_count":17,"area":null},{"id":"2102.10570","title":"Symbolic regression for scientific discovery: an application to wind speed forecasting","created":"2021-02-21","cited_by_count":16,"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":"2310.03652","title":"Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics","created":"2023-10-05","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":"2503.23616","title":"Interpretable Machine Learning in Physics: A Review","created":"2025-03-30","cited_by_count":14,"area":null},{"id":"2210.08095","title":"Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty","created":"2022-10-14","cited_by_count":13,"area":null},{"id":"2402.18603","title":"MMSR: Symbolic Regression is a Multi-Modal Information Fusion Task","created":"2024-02-28","cited_by_count":13,"area":null},{"id":"2103.15147","title":"Symbolic regression outperforms other models for small data sets","created":"2021-03-28","cited_by_count":12,"area":null},{"id":"2306.17396","title":"Koopman operator learning using invertible neural networks","created":"2023-06-30","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":"2410.05592","title":"Training Stiff Neural Ordinary Differential Equations with Implicit Single-Step Methods","created":"2024-10-08","cited_by_count":11,"area":null},{"id":"2105.06331","title":"Informed Equation Learning","created":"2021-05-13","cited_by_count":10,"area":null},{"id":"2106.00026","title":"Machine-Learning Non-Conservative Dynamics for New-Physics Detection","created":"2021-05-31","cited_by_count":10,"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":"2307.13149","title":"Discovering interpretable elastoplasticity models via the neural polynomial method enabled symbolic regressions","created":"2023-07-24","cited_by_count":10,"area":null},{"id":"2401.14424","title":"Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search","created":"2024-01-24","cited_by_count":10,"area":null},{"id":"2411.04383","title":"Neuro-Symbolic AI: Explainability, Challenges, and Future Trends","created":"2024-11-07","cited_by_count":10,"area":null},{"id":"2312.04070","title":"A Transformer Model for Symbolic Regression towards Scientific Discovery","created":"2023-12-07","cited_by_count":9,"area":null},{"id":"2406.09740","title":"Deep Symbolic Optimization for Combinatorial Optimization: Accelerating Node Selection by Discovering Potential Heuristics","created":"2024-06-14","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":"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":"2505.16477","title":"Advancing the Scientific Method with Large Language Models: From Hypothesis to Discovery","created":"2025-05-22","cited_by_count":7,"area":null},{"id":"2509.03036","title":"Knowledge Integration for Physics-informed Symbolic Regression Using Pre-trained Large Language Models","created":"2025-09-03","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":"2308.10892","title":"Bayesian polynomial neural networks and polynomial neural ordinary differential equations","created":"2023-08-17","cited_by_count":6,"area":null},{"id":"2401.11825","title":"Sparse discovery of differential equations based on multi-fidelity Gaussian process","created":"2024-01-22","cited_by_count":5,"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":"2406.03585","title":"A Comparison of Recent Algorithms for Symbolic Regression to Genetic Programming","created":"2024-06-05","cited_by_count":5,"area":null},{"id":"2412.01181","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","created":"2024-12-02","cited_by_count":5,"area":null},{"id":"2103.13496","title":"Similarity-Based Equational Inference in Physics","created":"2021-03-24","cited_by_count":4,"area":null},{"id":"2209.10861","title":"A novel corrective-source term approach to modeling unknown physics in aluminum extraction process","created":"2022-09-22","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":"2505.15572","title":"Bridging the Domain Gap in Equation Distillation with Reinforcement Feedback","created":"2025-05-21","cited_by_count":4,"area":null},{"id":"2506.15881","title":"T-SHRED: Symbolic Regression for Regularization and Model Discovery with Transformer Shallow Recurrent Decoders","created":"2025-06-18","cited_by_count":4,"area":null},{"id":"2506.18339","title":"Structured Kolmogorov-Arnold Neural ODEs for Interpretable Learning and Symbolic Discovery of Nonlinear Dynamics","created":"2025-06-23","cited_by_count":4,"area":null},{"id":"2405.02369","title":"No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks","created":"2024-05-03","cited_by_count":3,"area":null},{"id":"2511.08570","title":"Automatic Grid Updates for Kolmogorov-Arnold Networks using Layer Histograms","created":"2025-11-11","cited_by_count":3,"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":"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":"2412.11139","title":"ViSymRe: Vision Multimodal Symbolic Regression","created":"2024-12-15","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":"2508.21484","title":"Data-driven Discovery of Digital Twins in Biomedical Research","created":"2025-08-29","cited_by_count":2,"area":null},{"id":"2605.04337","title":"Symbolic Regression via Neural Networks","created":"2026-05-05","cited_by_count":2,"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":"2406.14844","title":"DN-CL: Deep Symbolic Regression against Noise via Contrastive Learning","created":"2024-06-21","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":"2504.05756","title":"Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression","created":"2025-04-08","cited_by_count":1,"area":null},{"id":"2504.07618","title":"CTSR: Cartesian tensor-based sparse regression for data-driven discovery of high-dimensional invariant governing equations","created":"2025-04-10","cited_by_count":1,"area":null},{"id":"2504.16503","title":"Neuro-Evolutionary Approach to Physics-Aware Symbolic Regression","created":"2025-04-23","cited_by_count":1,"area":null},{"id":"2507.17777","title":"ASP-Assisted Symbolic Regression: Uncovering Hidden Physics in Fluid Mechanics","created":"2025-07-22","cited_by_count":1,"area":null},{"id":"2508.19487","title":"Data-Efficient Symbolic Regression via Foundation Model Distillation","created":"2025-08-27","cited_by_count":1,"area":null},{"id":"2509.21341","title":"From Embeddings to Equations: Genetic-Programming Surrogates for Interpretable Transformer Classification","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2512.12448","title":"Optimized Architectures for Kolmogorov-Arnold Networks","created":"2025-12-13","cited_by_count":1,"area":null},{"id":"2603.06869","title":"Symmetry-Constrained Language-Guided Program Synthesis for Discovering Governing Equations from Noisy and Partial Observations","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2605.12704","title":"FePySR: A Neural Feature Extraction Framework for Efficient and Scalable Symbolic Regression","created":"2026-05-12","cited_by_count":1,"area":null},{"id":"2607.27794","title":"Can AI Follow In Einstein's Footsteps?","created":"2026-07-30","cited_by_count":1,"area":null},{"id":"2102.13388","title":"Zoetrope Genetic Programming for Regression","created":"2021-02-26","cited_by_count":0,"area":null},{"id":"2303.13746","title":"Achieving Occam's Razor: Deep Learning for Optimal Model Reduction","created":"2023-03-24","cited_by_count":0,"area":null},{"id":"2402.18040","title":"Automated Discovery of Integral with Deep Learning","created":"2024-02-28","cited_by_count":0,"area":null},{"id":"2505.06091","title":"UniSymNet: A Unified Symbolic Network Guided by Transformer","created":"2025-05-09","cited_by_count":0,"area":null},{"id":"2506.15715","title":"NeuronSeek: On Stability and Expressivity of Task-driven Neurons","created":"2025-06-01","cited_by_count":0,"area":null},{"id":"2506.20056","title":"Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization","created":"2025-06-24","cited_by_count":0,"area":null},{"id":"2510.05178","title":"Auditable Unit-Aware Thresholds in Symbolic Regression via Logistic-Gated Operators","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2602.09530","title":"Learning to Discover Iterative Spectral Algorithms","created":"2026-02-10","cited_by_count":0,"area":null},{"id":"2603.13727","title":"Data-driven Progressive Discovery of Physical Laws","created":"2026-03-14","cited_by_count":0,"area":null},{"id":"2605.01067","title":"Deep Variational Inference Symbolic Regression","created":"2026-05-01","cited_by_count":0,"area":null},{"id":"2607.08150","title":"DeepPySR -- A Symbolic Regression Framework with Dynamic Pruning, Pareto Selection, and Hierarchical Composition for Real-World Scientific Discovery","created":"2026-07-09","cited_by_count":0,"area":null},{"id":"2608.28408","title":"SymboLLM-FE: LLM-Accelerated Symbolic Regression for Automated Feature Engineering on Tabular Data","created":"2026-08-28","cited_by_count":0,"area":null}],"next_cursor":null}