symbolic regression

Predicting Ordinary Differential Equations with Transformers ICML 2023 Rethinking Symbolic Regression: Morphology and Adaptability in the Context of Evolutionary Algorithms ICLR 2023 Symbolic Explanations for Hyperparameter Optimization AutoML 2023 Symbolic Metamodels for Interpreting Black-Boxes Using Primitive Functions AAAI 2023 Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search ICLR 2023 Transformer-based model for symbolic regression via joint supervised learning ICLR 2023 Transformer-based Planning for Symbolic Regression NeurIPS 2023 Universal Physics-Informed Neural Networks: Symbolic Differential Operator Discovery with Sparse Data ICML 2023 What Can We Learn by Predicting Accuracy? WACV 2023 A Unified Framework for Deep Symbolic Regression NeurIPS 2022 CoNSoLe: Convex Neural Symbolic Learning NeurIPS 2022 D-CODE: Discovering Closed-form ODEs from Observed Trajectories ICLR 2022 Deep symbolic regression for recurrence prediction ICML 2022 End-to-end Symbolic Regression with Transformers NeurIPS 2022 Interactive Reinforcement Learning for Symbolic Regression from Multi-Format Human-Preference Feedbacks IJCAI 2022 Symbolic Distillation for Learned TCP Congestion Control NeurIPS 2022 Symbolic Learning to Optimize: Towards Interpretability and Scalability ICLR 2022 A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics NeurIPS 2021 Automated Symbolic Law Discovery: A Computer Vision Approach AAAI 2021 Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients ICLR 2021 Logic Guided Genetic Algorithms (Student Abstract) AAAI 2021 Neural Symbolic Regression that scales ICML 2021 Physics-informed Spline Learning for Nonlinear Dynamics Discovery IJCAI 2021 Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding NeurIPS 2021 AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity NeurIPS 2020