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Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning
However, its performance is very dependent on the training data and performs poorly on data outside the training set, which leads to poor noise robustness and Versatility of such methods.
MMSR: Symbolic Regression is a Multimodal Task
The SR problem is solved as a pure multimodal problem, and contrastive learning is also introduced in the training process for modal alignment to facilitate later modal feature fusion.
Discovering Mathematical Formulas from Data via GPT-guided Monte Carlo Tree Search
To optimize the trade-off between efficiency and versatility, we introduce SR-GPT, a novel algorithm for symbolic regression that integrates Monte Carlo Tree Search (MCTS) with a Generative Pre-Trained Transformer (GPT).
MetaSymNet: A Dynamic Symbolic Regression Network Capable of Evolving into Arbitrary Formulations
To address these issues, we propose MetaSymNet, a novel neural network that dynamically adjusts its structure in real-time, allowing for both expansion and contraction.
