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

https://github.com/sichao-li/diverse-explanations-physical-sciences

공식 코드

Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical Sciences

https://github.com/sjwetzel/publicsymbolicnninterpretation

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Closed-Form Interpretation of Neural Network Classifiers with Symbolic Gradients

https://github.com/smiles724/3d-transformer

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Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs

https://github.com/smiles724/molformer

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Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs

https://github.com/snap-stanford/biodiscoveryagent

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BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments

https://github.com/sspring137/EagleEye

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Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip Statistics

https://github.com/strong-ai-lab/abductionrules

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AbductionRules: Training Transformers to Explain Unexpected Inputs

https://github.com/technion-kishony-lab/data-to-paper

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Autonomous LLM-driven research from data to human-verifiable research papers

https://github.com/teddy-xionggz/truthhypo

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Toward Reliable Biomedical Hypothesis Generation: Evaluating Truthfulness and Hallucination in Large Language Models

https://github.com/tensorflow/gan/tree/master/tensorflow_gan/examples/dme_cyclegan

공식 코드

Scientific Discovery by Generating Counterfactuals using Image Translation

https://github.com/thephet/SciTransformer

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Predicting Real-time Scientific Experiments Using Transformer models and Reinforcement Learning

https://github.com/thu-esis/jiuzhou

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ClimateChat: Designing Data and Methods for Instruction Tuning LLMs to Answer Climate Change Queries

https://github.com/thudm/sciglm

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SciInstruct: a Self-Reflective Instruction Annotated Dataset for Training Scientific Language Models

https://github.com/tsinghuac3i/llm4biohypogen

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Large Language Models as Biomedical Hypothesis Generators: A Comprehensive Evaluation

https://github.com/ulab-uiuc/safescientist

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SafeScientist: Toward Risk-Aware Scientific Discoveries by LLM Agents

https://github.com/ur-whitelab/llms-in-science

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A Review of Large Language Models and Autonomous Agents in Chemistry

https://github.com/usail-hkust/llm-mm-agent

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MM-Agent: LLM as Agents for Real-world Mathematical Modeling Problem

https://github.com/vanderschaarlab/deepgenerativesymbolicregression

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Deep Generative Symbolic Regression

https://github.com/vganapati/led_pvae

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Data-Driven Computational Imaging for Scientific Discovery

https://github.com/wangzhaoyu07/fedc2sl

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Towards Practical Federated Causal Structure Learning

https://github.com/wanhaoliu/chemsimx

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MOOSE-Chem3: Toward Experiment-Guided Hypothesis Ranking via Simulated Experimental Feedback

https://github.com/webeng/llm_knowledge_gap_finder

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Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps

https://github.com/wenyueh/inductive_reasoning_benchmark

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InductionBench: LLMs Fail in the Simplest Complexity Class

https://github.com/willdreamer/awesome-ai4cfd

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Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey

https://github.com/wiseodd/bo-async-feedback

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How Useful is Intermittent, Asynchronous Expert Feedback for Bayesian Optimization?

https://github.com/wmyw96/fair

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Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning

https://github.com/wyu54/many-body-force-infer

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Physics-tailored machine learning reveals unexpected physics in dusty plasmas

https://github.com/xingjunm/awesome-large-model-safety

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Safety at Scale: A Comprehensive Survey of Large Model Safety

https://github.com/yijia-xiao/protein-llm-survey

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Protein Large Language Models: A Comprehensive Survey

https://github.com/ying531/selcf_paper

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Selection by Prediction with Conformal p-values

https://github.com/yu-fangxu/for

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Flow of Reasoning:Training LLMs for Divergent Problem Solving with Minimal Examples

https://github.com/yuzhimanhua/Awesome-Scientific-Language-Models

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A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery

https://github.com/zequnl/graphex

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Graphine: A Dataset for Graph-aware Terminology Definition Generation

https://github.com/ziatdinovmax/neurobayes

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Active Learning with Fully Bayesian Neural Networks for Discontinuous and Nonstationary Data

https://github.com/zyzisastudyreallyhardguy/llm4sd

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Large Language Models for Scientific Synthesis, Inference and Explanation

https://huggingface.co/zequnl/molxpt

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MolXPT: Wrapping Molecules with Text for Generative Pre-training

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