https://github.com/KuoTzu-yang/DeepMutation
Adversarial Sample Detection for Deep Neural Network through Model Mutation 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/KuoTzu-yang/DeepMutationOperators
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
https://github.com/SJ001/AI-Feynman
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
https://github.com/ScottJordan/SafePolicyImprovementNonstationary
https://github.com/alex98chen/testGAN
PacGAN: The power of two samples in generative adversarial networks
https://github.com/alexanderlynl/safestats
https://github.com/antoninschrab/dpkernel-paper
https://github.com/antoninschrab/mmdagg-paper
https://github.com/asonabend/dtr_via_surrogate_loss
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
https://github.com/avinash0309/BRAIN-TUMOR-SEGEMENTATION-OF-LOW-GRADE-GLIOMAS
https://github.com/bbwang1030/half-kfn
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
https://github.com/bhargavajs07/Packed-Wasserstein-GAN-with-GradientPenalty-Example
PacGAN: The power of two samples in generative adversarial networks
https://github.com/bhargavajs07/Packed_WGAN_GP_Example
PacGAN: The power of two samples in generative adversarial networks
https://github.com/edwarddavis1/universal_dynamic_embedding_with_testing
https://github.com/feiwu77777/Leveraging-Model-Interpretability-and-Stability-to-increase-Model-Robustness
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
https://github.com/fjxmlzn/DoppelGANger
PacGAN: The power of two samples in generative adversarial networks
https://github.com/hcllaw/private_tst
https://github.com/jjfeng/aACP
https://github.com/kacperChwialkowski/analyticMeanEmbeddings
Fast Two-Sample Testing with Analytic Representations of Probability Measures
https://github.com/lee-group-cmu/lf2i
Confidence Sets and Hypothesis Testing in a Likelihood-Free Inference Setting
https://github.com/marcodeangelis/Area-metric
On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests
https://github.com/mateuszbuda/brain-segmentation-pytorch
https://github.com/neurodata/MGC-paper
https://github.com/neurodata/hyppo
https://github.com/neurodata/mgc
https://github.com/neurodata/mgcpy
https://github.com/richterleo/Auditing_Test_for_LMs
https://github.com/sarahromanes/multiDA
Diagonal Discriminant Analysis with Feature Selection for High Dimensional Data
https://github.com/siddharth-agrawal/Generative-Moment-Matching-Networks
https://github.com/sshekhar17/PermFreeMMD
https://github.com/sshekhar17/nonparametric-testing-by-betting
Sequential Nonparametric Testing with the Law of the Iterated Logarithm
https://github.com/tim-coleman/RFtest
Scalable and Efficient Hypothesis Testing with Random Forests
https://github.com/tim-coleman/SURFTest
Scalable and Efficient Hypothesis Testing with Random Forests
https://github.com/tkuo-tkuo/DeepMutationOperators
Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing
https://github.com/wilsoncai1992/adaptest
Data-adaptive statistics for multiple hypothesis testing in high-dimensional settings
https://github.com/woodyx218/Deep-Learning-with-GDP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.