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Related Experiment Video

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

Contrastive diffusion model for exploring mathematical expressions from data.

Canmiao Zhou1, Han Huang2, Xueming Yan3

  • 1School of Software Engineering, South Chine University of Technology, Guangzhou, 510006, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 8, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel non-autoregressive diffusion model for symbolic regression. The approach enhances global semantic understanding, leading to superior performance in discovering accurate and simple mathematical expressions from data.

Keywords:
Contrastive learningDiffusion modelNeural networkSymbolic regression

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Published on: February 9, 2017

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Synthesis of Cyclic Polymers and Characterization of Their Diffusive Motion in the Melt State at the Single Molecule Level

Published on: September 26, 2016

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Mathematics

Background:

  • Symbolic regression aims to find mathematical expressions fitting data, crucial for science and engineering.
  • Current deep generative models use autoregressive methods, struggling with the global structure of expressions.
  • Unidirectional dependencies in autoregressive models limit capturing complex, bidirectional relationships within mathematical formulas.

Purpose of the Study:

  • To develop an advanced non-autoregressive approach for symbolic regression using diffusion models.
  • To improve the modeling of global semantic logic in mathematical expression generation.
  • To enhance the utilization of multimodal data and semantic understanding through contrastive learning.

Main Methods:

  • An iterative non-autoregressive diffusion model was developed to generate entire mathematical expressions in a latent space.
  • Contrastive learning was integrated to align data point features with symbolic expression features, reducing modal discrepancies.
  • The model refines expression generation by considering global semantic relationships.

    • Main Results:

    • The proposed diffusion model significantly outperforms existing mainstream baselines on benchmark datasets.
    • The model efficiently discovers mathematical expression solutions, achieving superior fitting accuracy.
    • The approach ensures a high degree of simplicity in the generated mathematical expressions.

    Conclusions:

    • The non-autoregressive diffusion model effectively captures the global perspective of mathematical expressions.
    • This method offers an efficient and powerful solution for the challenging symbolic regression problem.
    • Integrating contrastive learning enhances multimodal data utilization and semantic understanding for improved generation.