https://github.com/marwaltz/tud_rl
공식 코드Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing
Papers with Code 아카이브
Two-sample testing
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In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant. The statistics used in two-sample tests can be used to solve many machine learning problems, such as domain adaptation, covariate shift and generative adversarial networks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mateuszbuda/brain-segmentation
공식 코드https://github.com/meyerscetbon/L1_test
공식 코드Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing
https://github.com/meyerscetbon/l1_two_sample_test
공식 코드Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing
https://github.com/microsoft/goodpoints
공식 코드Compress Then Test: Powerful Kernel Testing in Near-linear Time
https://github.com/mkirchler/deep-2-sample-test
공식 코드https://github.com/namratadeka/mmd-b-fair
공식 코드MMD-B-Fair: Learning Fair Representations with Statistical Testing
https://github.com/paul-sheridan/hgt-tfidf
공식 코드The hypergeometric test performs comparably to TF-IDF on standard text analysis tasks
https://github.com/pfilonenko/ml_for_twosampletesting
공식 코드Machine Learning for Two-Sample Testing under Right-Censored Data: A Simulation Study
https://github.com/ryu577/stochproc
공식 코드https://github.com/sarath1789/ahtg_neurips2019
공식 코드https://github.com/shubhobm/isw
공식 코드https://github.com/siqil/CPPOD
공식 코드https://github.com/smkia/interpretability
공식 코드Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification
https://github.com/soltoggio/Short-term-plasticity-as-hypothesis-testing-in-distal-reward-learning
공식 코드Short-term plasticity as cause-effect hypothesis testing in distal reward learning
https://github.com/sr-11/lfi
공식 코드https://github.com/sss1/SobolevEstimation
공식 코드https://github.com/steverab/failing-loudly
공식 코드Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
https://github.com/suman-cha/cond2st
공식 코드https://github.com/uk-cliplab/representationjsd
공식 코드https://github.com/vonguyenleduy/dnn_representation_selective_inference
공식 코드https://github.com/vonguyenleduy/dnn_segmentation_selective_inference
공식 코드https://github.com/wanrongz/PAPRIKA
공식 코드https://github.com/wayne0908/Label-Efficient-Two-Sample
공식 코드https://github.com/wbounliphone/Ustatistics_Approach_For_SD
공식 코드A U-statistic Approach to Hypothesis Testing for Structure Discovery in Undirected Graphical Models
https://github.com/wittawatj/kernel-cgof
공식 코드Testing Goodness of Fit of Conditional Density Models with Kernels
https://github.com/wojzaremba/btest
공식 코드https://github.com/wxiao0421/onlineRPCA
공식 코드Online Robust Principal Component Analysis with Change Point Detection
https://github.com/yromano/fair_dummies
공식 코드https://github.com/yujiali/gmmn
공식 코드https://zenodo.org/record/8077639
공식 코드Multimodal Multi-User Surface Recognition with the Kernel Two-Sample Test
https://github.com/Abhipanda4/GMMN-Pytorch
https://github.com/AlexVerine/PrecisionRecallGan
Revisiting Precision and Recall Definition for Generative Model Evaluation
https://github.com/BaiqingL/CS523-Final
https://github.com/Craven-Biostat-Lab/mihifepe
Understanding Learned Models by Identifying Important Features at the Right Resolution
https://github.com/GiuliaLavizzari/ML4thesis
Variational Autoencoders for New Physics Mining at the Large Hadron Collider
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.