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Experimental Design

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코드

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

https://github.com/tsinghuac3i/ultramedical

Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine

https://github.com/tsudalab/PrefInt

Leveraging Legacy Data to Accelerate Materials Design via Preference Learning

https://github.com/tueboesen/Active-Learning

A-Optimal Active Learning

https://github.com/vvvityaaa/vvvityaaa

Multi-Coil MRI Reconstruction Challenge -- Assessing Brain MRI Reconstruction Models and their Generalizability to Varying Coil Configurations

https://github.com/wgshen/sOED

Sequential Bayesian optimal experimental design via approximate dynamic programming

https://github.com/yangpuPKU/L2SR-Learning-to-Sample-and-Reconstruct

Experimental design for MRI by greedy policy search

https://github.com/yaroslav-moiseev/evidence-based-possibly-best-practices-in-classical-ML

A Meta-Analysis of the Anomaly Detection Problem

https://github.com/ypwang61/VAS

Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning

https://github.com/ypwang61/negcliploss_normsim

Variance Alignment Score: A Simple But Tough-to-Beat Data Selection Method for Multimodal Contrastive Learning

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