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Found 9 papers, 1 papers with code
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.
PruneSymNet: A Symbolic Neural Network and Pruning Algorithm for Symbolic Regression
Therefore, a greedy pruning algorithm is proposed to prune the network into a subnetwork while ensuring the accuracy of data fitting.
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).
A Novel Paradigm for Neural Computation: X-Net with Learnable Neurons and Adaptable Structure
1, The type of activation function is single and relatively fixed, which leads to poor "unit representation ability" of the network, and it is often used to solve simple problems with very complex networks; 2, the network structure is not adaptive, it is easy to cause network structure redundant or insufficient.
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.
A Neural-Guided Dynamic Symbolic Network for Exploring Mathematical Expressions from Data
Instead of searching for expressions within a large search space, we explore DySymNet with various structures and optimize them to identify expressions that better-fitting the data.
Atrous Residual Interconnected Encoder to Attention Decoder Framework for Vertebrae Segmentation via 3D Volumetric CT Images
The proposed model is based on the structure of encoder to decoder, using layer normalization to optimize mini-batch training performance.
A game theoretical approach to homothetic robust forward investment performance processes in stochastic factor models
This paper studies an optimal forward investment problem in an incomplete market with model uncertainty, in which the underlying stocks depend on the correlated stochastic factors.
