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

https://github.com/giovanniguidi/logo-detection

A Deep One-Shot Network for Query-based Logo Retrieval

https://github.com/helenzhao093/MLMethods

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform

https://github.com/horbachmp/TensorBandits

Stochastic Low-rank Tensor Bandits for Multi-dimensional Online Decision Making

https://github.com/i-yamane/uplift

Uplift Modeling from Separate Labels

https://github.com/iCompass-ai/TUNIZI

TUNIZI: a Tunisian Arabizi sentiment analysis Dataset

https://github.com/ivul-kaust/mole

TUNIZI: a Tunisian Arabizi sentiment analysis Dataset

https://github.com/jjfeng/aACP

Approval policies for modifications to Machine Learning-Based Software as a Medical Device: A study of bio-creep

https://github.com/johaupt/response-dependent-costs

Targeting customers under response-dependent costs

https://github.com/ko-fujita/ko-fujita

Fatigue-Aware Ad Creative Selection

https://github.com/olety/TIMLinUCB

Online Influence Maximization in Non-Stationary Social Networks

https://github.com/orestislampridis/MANIFEST

My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social Networks

https://github.com/phixion/phixion

Explaining Large Language Models Decisions Using Shapley Values

https://github.com/rafaelpalmerio/causal_forest

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

https://github.com/rvlopes/gloria

Scaling Language Models: Methods, Analysis & Insights from Training Gopher

https://github.com/simuler/MetaHeac

Learning to Expand Audience via Meta Hybrid Experts and Critics for Recommendation and Advertising

https://github.com/smazzanti/mrmr

Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform

https://github.com/swager/causalForest

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

https://github.com/till-tietz/rcf

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

https://github.com/ucsd-dsc-arts/dsc160-final-dsc160_final_group8

Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts

https://github.com/vshirvaikar/rrcf

Estimation and Inference of Heterogeneous Treatment Effects using Random Forests

https://github.com/wilsoncai1992/adaptest

Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings

https://github.com/wywywang/coachai-projects

ShuttleSHAP: A Turn-Based Feature Attribution Approach for Analyzing Forecasting Models in Badminton

https://github.com/yukikongju/R-Tutorial

Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey

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