Neural Symbollic Regression Using Deep Learning and Sparse Modelling

arXiv:2609.01102 · cs.LG, cs.AI, cs.NE, cs.SC · Submitted 2026-09-01 · Read on arXiv

cs.LG, cs.AI, cs.NE, cs.SC

Submitted: 2026-09-01

Updated: 2026-09-01

Terminology

• Symbolic Regression — 40× in this paper · explained in 5 episodes
• Benchmark — 27× in this paper · explained in 2 episodes
• Interpretability — 15× in this paper · explained in 15 episodes
• Neural Symbolic Regression — 13× in this paper
• Robustness — 13× in this paper · explained in 5 episodes
• Accuracy — 11× in this paper · explained in 1 episode
• Generalization — 10× in this paper · explained in 13 episodes
• Genetic Programming — 8× in this paper
• Ablation — 7× in this paper · explained in 1 episode
• Sparsity — 7× in this paper · explained in 4 episodes
• Transformer — 5× in this paper · explained in 2 episodes
• Regularization — 4× in this paper · explained in 3 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Pruning — 2× in this paper · explained in 4 episodes
• Reinforcement Learning — 2× in this paper · explained in 4 episodes
• Root Mean Squared Error — 2× in this paper
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• AdamW Optimizer — 1× in this paper · explained in 1 episode
• Adaptability — 1× in this paper · explained in 1 episode
• Approximation Bound — 1× in this paper · explained in 1 episode
• Bayesian Optimization — 1× in this paper · explained in 3 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Meta-learning — 1× in this paper · explained in 2 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• System Identification — 1× in this paper · explained in 3 episodes
• alpha — 1× in this paper · explained in 1 episode

Sources

• Contemporary Symbolic Regression Methods and their Relative Performance
• Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
• Neural Symbolic Regression that Scales
• Solute drag forces from equilibrium interface fluctuations

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