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Symbolic Regression
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producing a mathematical expression (symbolic expression) that fits a given tabular data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/carolinepacheco/automated-equation-discovery-scene-analysis
Automated Mathematical Equation Structure Discovery for Visual Analysis
https://github.com/dbkgroup/GVAE4Smiles
https://github.com/dso-org/deep-symbolic-optimization
https://github.com/emi-group/evox
EvoGP: A GPU-accelerated Framework for Tree-based Genetic Programming
https://github.com/glouppe/info8010-deep-learning
Discovering Symbolic Models from Deep Learning with Inductive Biases
https://github.com/harveyThomas4692/llmlex
Symbolic Regression with Multimodal Large Language Models and Kolmogorov Arnold Networks
https://github.com/jhcircle/less-is-more
LLM-SR: Scientific Equation Discovery via Programming with Large Language Models
https://github.com/juangamella/causal-chamber-package
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology
https://github.com/marcovirgolin/SimpleGP
Learning a Formula of Interpretability to Learn Interpretable Formulas
https://github.com/mkusner/grammarVAE
https://github.com/mojivalipour/symbolicgpt
SymbolicGPT: A Generative Transformer Model for Symbolic Regression
https://github.com/omerronen/scales
https://github.com/rmojgani/rmojgani
https://github.com/snel-repo/expressive-latent-dynamics-paper
Chaos as an interpretable benchmark for forecasting and data-driven modelling
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.