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Publicbootstrap
Publicmarkdown-extract
Publicexplainable-lms
Publicdistil-whisper
Publiclanguage-model-service
PublicRobust-Entropy-Search
Publicstorm
Publicswagger-ui
Publicpunctuator2
Publiclua-resty-string
PublicYouTokenToMe
Publicinformed_nlu
PublicMANR
Publicarabic_nlp
Publicwhisper
Publicsilero-vad
PublicMaxSimE
Publicspeechcatcher
Publicjson-nlohmann
PublicBUTSpeechFIT-VBx
Publicre2
Publicgsoap2
Publicgit-svn clone of https://sourceforge.net/p/gsoap2/code/googletest
Publicprotobuf
Publicquantum_subset_sum
Publickaldi
PublicOpenBLAS
Publicwasserstein-dropout
PublicWasserstein dropout (W-dropout) is a novel technique to quantify uncertainty in regression networks. It is fully non-parametric and yields accurate uncertainty estimates - even under data shifts.