https://github.com/computationalprivacy/mia_llms_benchmark
공식 코드SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)
Papers with Code 아카이브
Experimental Design
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/computproteomics/omicsq
공식 코드OmicsQ: A User-Friendly Platform for Interactive Quantitative Omics Data Analysis
https://github.com/crharshaw/submodular-minus-linear
공식 코드Submodular Maximization Beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
https://github.com/csiro-funml/bacon
공식 코드https://github.com/csiro-mlai/RL-BOED
공식 코드Optimizing Sequential Experimental Design with Deep Reinforcement Learning
https://github.com/cwaisman/Parallel-Experimentation
공식 코드Parallel Experimentation and Competitive Interference on Online Advertising Platforms
https://github.com/daewoongham97/CRT
공식 코드Using Machine Learning to Test Causal Hypotheses in Conjoint Analysis
https://github.com/dallascard/NLP-power-analysis
공식 코드https://github.com/damo-nlp-sg/coi-agent
공식 코드Chain of Ideas: Revolutionizing Research Via Novel Idea Development with LLM Agents
https://github.com/darrow-labs/legallens
공식 코드LegalLens: Leveraging LLMs for Legal Violation Identification in Unstructured Text
https://github.com/desi-ivanova/idad
공식 코드Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods
https://github.com/downingtim/dimension_reduction_plots
공식 코드A primer on correlation-based dimension reduction methods for multi-omics analysis
https://github.com/dykang/PASTEL
공식 코드https://github.com/ebridge2/causal_neuro
공식 코드How causal perspectives can inform problems in computational neuroscience
https://github.com/educating-dip/bayesian_experimental_design
공식 코드Bayesian Experimental Design for Computed Tomography with the Linearised Deep Image Prior
https://github.com/evanmunro/market-interference
공식 코드https://github.com/facebookresearch/sustainableconcrete
공식 코드https://github.com/fastmachinelearning/hls4ml
공식 코드hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
https://github.com/fiezt/Transductive-Linear-Bandit-Code
공식 코드Sequential Experimental Design for Transductive Linear Bandits
https://github.com/fsotoc/grtools
공식 코드https://github.com/genedisco/genedisco
공식 코드GeneDisco: A Benchmark for Experimental Design in Drug Discovery
https://github.com/gokyeongryeol/tbu
공식 코드https://github.com/google-research/optformer
공식 코드https://github.com/gsbDBI/adaptive-confidence-intervals
공식 코드Confidence Intervals for Policy Evaluation in Adaptive Experiments
https://github.com/guangyaodou/eeg4students
공식 코드EEG4Students: An Experimental Design for EEG Data Collection and Machine Learning Analysis
https://github.com/hguan6/differential_diagnosis
공식 코드A Bayesian Approach for Medical Inquiry and Disease Inference in Automated Differential Diagnosis
https://github.com/hippylib/hippyflow
공식 코드Derivative-Informed Projected Neural Networks for High-Dimensional Parametric Maps Governed by PDEs
https://github.com/huangdaolang/aline
공식 코드ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition
https://github.com/huangdaolang/amortized-decision-aware-bed
공식 코드https://github.com/iolloj/pasoa
공식 코드https://github.com/ivaxi0s/hypothesizing-causal-variable-llm
공식 코드https://github.com/jajajang/lowpopart
공식 코드Efficient Low-Rank Matrix Estimation, Experimental Design, and Arm-Set-Dependent Low-Rank Bandits
https://github.com/jajajang/sparse
공식 코드PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits
https://github.com/jcopo/ContrastiveDiffusions
공식 코드https://github.com/jeffreych0/mental_chatbot_survey
공식 코드https://github.com/jeknov/RankME
공식 코드RankME: Reliable Human Ratings for Natural Language Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.