Bayesian Jin Learning adanwo

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

1. Bawo ni nẹtiwọọki neural Bayesian ṣe tọju awọn iwuwo rẹ yatọ si ọkan boṣewa?
   • O ṣe itọju iwuwo kọọkan bi pinpin iṣeeṣe dipo iye ti o wa titi ẹyọkan
   • O nlo odidi òṣuwọn dipo eleemewa
   • O yọ awọn iwuwo kuro patapata
   • O pin iwuwo kan kọja gbogbo awọn fẹlẹfẹlẹ
2. What can a Bayesian approach provide that a single fixed-weight network does not directly represent?
   • A guarantee that every confidence score is calibrated
   • A distribution of predictions that reflects uncertainty about model weights
   • A proof that the model will detect every unfamiliar input
   • A way to remove all noise from the data
3. How can epistemic uncertainty change as relevant, representative training data are added?
   • It is guaranteed to reach zero with any added data
   • It is identical to label noise
   • It may decrease under the chosen model and data assumptions
   • It always increases linearly
4. Kini idi ti awọn isunmọ bii itọkasi iyatọ tabi MCMC nilo?
   • Nitori awọn nẹtiwọọki nkankikan ko ni iṣẹ ipadanu
   • Nitori awọn GPU ko le ṣe afikun
   • Nitori awọn iwuwo gbọdọ jẹ odidi
   • Nitori ẹhin gangan lori awọn miliọnu awọn iwuwo jẹ aibikita
5. Kini yiyọkuro Monte Carlo ṣe lati ṣe iṣiro aidaniloju?
   • Ṣe itọju yiyọ kuro lọwọ ni akoko idanwo ati ṣiṣe nẹtiwọọki ni ọpọlọpọ igba, ni lilo itankale awọn abajade
   • Yipada yiyọ kuro ati ọkọ oju irin to gun
   • Yọ gbogbo awọn neuronu kuro patapata
   • Rọpo pipadanu pẹlu ariwo laileto
6. Itọkasi iyatọ ṣe atunṣe iṣiro ẹhin bi iru iṣoro wo?
   • Iṣoro yiyan
   • Iṣoro iṣapeye ti o dinku iyatọ KL (nipasẹ ELBO)
   • A funfun ID iṣapẹẹrẹ isoro pẹlu ko si ohun to
   • A hardware oniru isoro
7. How does Bayes’ rule relate a prior, likelihood, and posterior?
   • The posterior is the prior plus the likelihood, without normalization
   • It is the likelihood divided by the prior without normalization
   • The posterior is proportional to the prior times the likelihood
   • It is the fixed point estimate of the most likely weight vector
8. How does MCMC approximate a Bayesian neural-network posterior?
   • By training a single point estimate with dropout disabled
   • By ranking model outputs with a fixed rule
   • By minimizing an ELBO with a simpler variational family
   • By drawing samples through a Markov chain whose stationary distribution targets the posterior