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scientific discovery

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/Advanced-Research-Centre/YAQQ

Automated Gadget Discovery in Science

https://github.com/CODARcode/MGARD

Scalable Hybrid Learning Techniques for Scientific Data Compression

https://github.com/CP3-Origins/Things-to-bench

Interpretable Scientific Discovery with Symbolic Regression: A Review

https://github.com/HomoSapienLCY/Contextual_Regression

Contextual Regression: An Accurate and Conveniently Interpretable Nonlinear Model for Mining Discovery from Scientific Data

https://github.com/KrishnaswamyLab/GeometryAwareGenerativeAutoencoder

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds

https://github.com/Masao-Taketani/AI-Scientist-with-Local-LLMs

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

https://github.com/Masao-Taketani/LocalAgentLaboratory

Agent Laboratory: Using LLM Agents as Research Assistants

https://github.com/NeuroAI-HD/HD-GLIO

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

https://github.com/NeuroAI-HD/HD-GLIO-AUTO

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

https://github.com/PaddlePaddle/PaddleScience/tree/develop/examples/transformer4sr

A Transformer Model for Symbolic Regression towards Scientific Discovery

https://github.com/abdallaharar/PHYS-490-Project

Discovering physical concepts with neural networks

https://github.com/alexhernandezgarcia/gflownet

Multi-Fidelity Active Learning with GFlowNets

https://github.com/amir-hassan25/IdeaBench

IdeaBench: Benchmarking Large Language Models for Research Idea Generation

https://github.com/ballcap231/ML-Applied-for-Physics

Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling

https://github.com/cbellinger27/semi-MarkovRLForMatrials

Reinforcement Learning in a Physics-Inspired Semi-Markov Environment

https://github.com/cci-bonn/hd-glio

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

https://github.com/chicagohai/hypothesis-generation

HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation

https://github.com/chicagohai/hypothesis_generation

HypoBench: Towards Systematic and Principled Benchmarking for Hypothesis Generation

https://github.com/csinva/interpretable-autoprompting

Explaining Patterns in Data with Language Models via Interpretable Autoprompting

https://github.com/csinva/local-vae

Transformation Importance with Applications to Cosmology

https://github.com/davidar/bib

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

https://github.com/deep-symbolic-mathematics/TPSR

Neural Symbolic Regression that Scales

https://github.com/fd17/SciNet_PyTorch

Discovering physical concepts with neural networks

https://github.com/gregoritoo/ABCDflow

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

https://github.com/guijinSON/SPOT

When AI Co-Scientists Fail: SPOT-a Benchmark for Automated Verification of Scientific Research

https://github.com/jamesrobertlloyd/gpss-research

Structure Discovery in Nonparametric Regression through Compositional Kernel Search

https://github.com/jantzen/eugene

Dynamical Kinds and their Discovery

https://github.com/jhcircle/less-is-more

LLM-SR: Scientific Equation Discovery via Programming with Large Language Models

https://github.com/k-woodruff/PMNSnet

Discovering physical concepts with neural networks

https://github.com/klin059/lits

Automated Design of Deep Learning Methods for Biomedical Image Segmentation

https://github.com/lamm-mit/pdf2audio

SciAgents: Automating scientific discovery through multi-agent intelligent graph reasoning

https://github.com/matlab-deep-learning/Physical-Concepts-Scinet

Discovering physical concepts with neural networks

https://github.com/microsoft/trace

Improving Parallel Program Performance with LLM Optimizers via Agent-System Interfaces

https://github.com/mindspore-ai/contrib/tree/master/application/SciNet

Discovering physical concepts with neural networks

https://github.com/nikivanstein/LLaMEA

LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms

https://github.com/osu-nlp-group/scienceagentbench

ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.