https://github.com/SK-tklab/BayesianOptimization
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
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/XanaduAI/pennylane
Better than classical? The subtle art of benchmarking quantum machine learning models
https://github.com/aashish-khub/NearestNeighbors
https://github.com/amzn/emukit
https://github.com/cbskust/SDS_Epidemic
Learning a nonlinear dynamical system model of gene regulation: A perturbed steady-state approach
https://github.com/chaplenf/microBiome-v3.4
Creation and analysis of biochemical constraint-based models: the COBRA Toolbox v3.0
https://github.com/dmolitor/pyssed
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
https://github.com/florianmahner/object-dimensions
https://github.com/fusion-ml/trajectory-information-rl
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/genedisco/genedisco-starter
GeneDisco: A Benchmark for Experimental Design in Drug Discovery
https://github.com/gtboauthors/gtbo
Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design
https://github.com/holarissun/rewardmodelingbeyondbradleyterry
Reviving The Classics: Active Reward Modeling in Large Language Model Alignment
https://github.com/htjb/margarine
https://github.com/jadenfiotto-kaufman/nnsight
NNsight and NDIF: Democratizing Access to Open-Weight Foundation Model Internals
https://github.com/jessevig/bertviz
https://github.com/jkbren/ps-gpucbpe
Optimal design of experiments to identify latent behavioral types
https://github.com/lamalab-org/chem-bench
Probing the limitations of multimodal language models for chemistry and materials research
https://github.com/lamalab-org/chembench
Probing the limitations of multimodal language models for chemistry and materials research
https://github.com/lamourj/fastcode-gpucb
Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design
https://github.com/microsoft/promptbase
Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine
https://github.com/neubig/nlp-from-scratch-assignment-2022
https://github.com/pearl-utexas/sope
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning
https://github.com/raminrasoulinezhad/DeepBenchVerilog
hls4ml: An Open-Source Codesign Workflow to Empower Scientific Low-Power Machine Learning Devices
https://github.com/rehoss/lbmpc_semimarkov
An Experimental Design Perspective on Model-Based Reinforcement Learning
https://github.com/rmcantin/bayesopt
https://github.com/sangyx/gtrick/tree/main/benchmark/pyg
https://github.com/scorebo/scorebo
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization
https://github.com/shalijiang/bo
BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design
https://github.com/sharad461/MKA-hallucination
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
https://github.com/simplaj/AI-Researcher-Spark
Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
https://github.com/stevenkleinegesse/bedimplicit
https://github.com/stevenkleinegesse/seqbed
Sequential Bayesian Experimental Design for Implicit Models via Mutual Information
https://github.com/sverdoot/regularized-dpp
Bayesian experimental design using regularized determinantal point processes
https://github.com/thomas-young-2013/mindware
https://github.com/thomas-young-2013/open-box
https://github.com/thomas-young-2013/soln-ml
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.