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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/listar2000/gfn-sr
공식 코드https://github.com/long-da/kbass
공식 코드Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
https://github.com/luningsun/splinelearningequation
공식 코드Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty
https://github.com/lurovi/survivalmultitree-pynsgp
공식 코드Interpretable Non-linear Survival Analysis with Evolutionary Symbolic Regression
https://github.com/marcovirgolin/GP-GOMEA
공식 코드Contemporary Symbolic Regression Methods and their Relative Performance
https://github.com/marcovirgolin/pyNSGP
공식 코드Learning a Formula of Interpretability to Learn Interpretable Formulas
https://github.com/mariodeflorio/ai-aristotle
공식 코드AI-Aristotle: A Physics-Informed framework for Systems Biology Gray-Box Identification
https://github.com/mariodeflorio/ai-lorenz
공식 코드https://github.com/mathbiocu/wsindy_cellcluster
공식 코드https://github.com/mattsheng/PAN_SR
공식 코드Ab Initio Nonparametric Variable Selection for Scalable Symbolic Regression with Large $p$
https://github.com/mec-lab/SRNE
공식 코드Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks
https://github.com/menggedu/edl
공식 코드LLM4ED: Large Language Models for Automatic Equation Discovery
https://github.com/mepx/mep-basic-src
공식 코드https://github.com/merlerm/in-context-symbolic-regression
공식 코드In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery
https://github.com/milescranmer/pysr_paper
공식 코드Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
https://github.com/milescranmer/symbolicregression.jl
공식 코드Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl
https://github.com/murphyzc/metakan
공식 코드Improving Memory Efficiency for Training KANs via Meta Learning
https://github.com/nisalr/ginn-lp
공식 코드https://github.com/nvcleemp/conjecturing
공식 코드https://github.com/omron-sinicx/srsd-benchmark
공식 코드Rethinking Symbolic Regression Datasets and Benchmarks for Scientific Discovery
https://github.com/omron-sinicx/transformer4sr
공식 코드A Transformer Model for Symbolic Regression towards Scientific Discovery
https://github.com/patrick-kidger/diffrax
공식 코드https://github.com/paulsonlab/torchsisso
공식 코드https://github.com/philipp238/parfam
공식 코드ParFam -- (Neural Guided) Symbolic Regression Based on Continuous Global Optimization
https://github.com/pongpisit-thanasutives/parametric-discovery
공식 코드https://github.com/punkduckable/PDE-READ
공식 코드PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning
https://github.com/punkduckable/pde-extraction
공식 코드PDE-READ: Human-readable Partial Differential Equation Discovery using Deep Learning
https://github.com/pySRURGS/pySRURGS
공식 코드https://github.com/rapidsai/cuml/tree/branch-23.10/cpp/include/cuml/genetic
공식 코드https://github.com/rose-stl-lab/symmetry-ode-discovery
공식 코드https://github.com/rtqichen/torchdiffeq
공식 코드https://github.com/samanthav24/dsc_fraud_detection
공식 코드Explainable Fraud Detection with Deep Symbolic Classification
https://github.com/samholt/deepgenerativesymbolicregression
공식 코드https://github.com/samuelkim314/DeepSymReg
공식 코드Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
https://github.com/samuelkim314/parametric-eql
공식 코드Deep Learning and Symbolic Regression for Discovering Parametric Equations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.