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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mlexchange/mlex
공식 코드https://github.com/mlresearchatosram/qprobing
공식 코드Quantitative probing: Validating causal models using quantitative domain knowledge
https://github.com/msmdev/plasmofab
공식 코드https://github.com/muellerlab/synthesizing_interpretable_control_policies
공식 코드Synthesizing Interpretable Control Policies through Large Language Model Guided Search
https://github.com/neuro-symbolic-ai/lunar-bioverse-demo
공식 코드An LLM-based Knowledge Synthesis and Scientific Reasoning Framework for Biomedical Discovery
https://github.com/neurodata/mgc-matlab
공식 코드https://github.com/nih-card/biomedsql
공식 코드BiomedSQL: Text-to-SQL for Scientific Reasoning on Biomedical Knowledge Bases
https://github.com/nikita-0209/mf-al-gfn
공식 코드https://github.com/nl2g/transformingsciencellms
공식 코드https://github.com/nrel/phase-space-sampling
공식 코드Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
https://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/open-sciencelab/social_science
공식 코드https://github.com/open-sciencelab/virtual-scientists
공식 코드https://github.com/opendfm/llm4chemistry
공식 코드From Generalist to Specialist: A Survey of Large Language Models for Chemistry
https://github.com/optima-cityu/llm4ad
공식 코드LLM4AD: A Platform for Algorithm Design with Large Language Model
https://github.com/ornl/flowcept
공식 코드Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability
https://github.com/prescient-design/nebula
공식 코드https://github.com/quackenbushlab/dash
공식 코드Pruning neural network models for gene regulatory dynamics using data and domain knowledge
https://github.com/ramanujanmachine/blind-delta-algorithm
공식 코드Unsupervised Discovery of Formulas for Mathematical Constants
https://github.com/rationalityenhancement/interpretablehumanplanning
공식 코드Automatic Discovery and Description of Human Planning Strategies
https://github.com/rkishony/data-to-paper-supplementary
공식 코드Autonomous LLM-driven research from data to human-verifiable research papers
https://github.com/rsinghlab/K-Paths
공식 코드K-Paths: Reasoning over Graph Paths for Drug Repurposing and Drug Interaction Prediction
https://github.com/runlongyu/pgfm
공식 코드Physics-Guided Foundation Model for Scientific Discovery: An Application to Aquatic Science
https://github.com/sakanaai/ai-scientist
공식 코드The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery
https://github.com/sakanaai/ai-scientist-iclr2025-workshop-experiment
공식 코드The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
https://github.com/sakanaai/ai-scientist-v2
공식 코드The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
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
https://github.com/sarinstein-yan/hsg-12m
공식 코드https://github.com/sarinstein-yan/poly2graph
공식 코드https://github.com/science-discovery/ai-newton
공식 코드AI-Newton: A Concept-Driven Physical Law Discovery System without Prior Physical Knowledge
https://github.com/sensein/structsense
공식 코드https://github.com/shalit-lab/deep-scientific-discovery
공식 코드Using Deep Networks for Scientific Discovery in Physiological Signals
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.