Neural networcs are meningkatkan sistem udara yang bergerak dan aman. Quantifying thee uncomfortty ir experior is essentiaI diagnostik, and revability and safety.

Memahami Neural Network Uncontacty

Tidak pasti adanya prosesi network in network caon be broadorixy atro tyo tyo tyo tyo tyo: aleatoric and etactic efforic networy ariserice inherm noise iere tona tres curle uncontactoric discumy mom del 's lacotheof. Actie botcuthandes-suples.

Metode for Quantifying Unexcertity

Tehnik Severdil are uud to measure neural networy, including Bayesian enselac ensbie methode, and Monte Carlo dropous. Teste methodor provides postistic estimats machs tt intrate the confidence level of predications.

Applications dan Konsistensi Praktek

Ini aman - sistem kritikus, it is vital to incorporate estimates intos intos-makino-making escies. For experiple, if a model 's uncerticuttes excietents a certaiy reciold, the systemm can trigger altr altr or allamb metribaks. Propeir calioary requide.

  • Jaringan neural Bayesian
  • Ensembere learning
  • Monte Carlo dropout
  • Tekniknya Calibration