Jàng bu xóot bu Bayesian
Jàngat bu xóot bu Bayesian dafay jàppee diisaayu reso neuronal bi ni distribution probabilite moo gën ni ay lim yu takku, suko defee model bi mën wax ni wóolu na.
Résumé
That matters for high-stakes uses — medicine, self-driving cars, finance — where 'I'm not sure' is a vital answer.
Plongeur bu xóot
Reseau neuronal buñ miin dafay jàng benn valeur fixe ci poid bu nekk; lu moy loolu, ab reso neuronal bayesian dafay jàng distribution ci kaw poids bu nekk, di jàpp lu wóorul ci luy valeur bu dëggu bi. Xalaat yi dañuy nekk moyenne ci kaw reso yu bari yu wóor, te loolu dafay jur wóolu seen bopp, du tontu rek. Ndax xayma ci ginaaw gi gëna jubal ci ay milioŋ ci poid yi, praktiseur yi dañuy jëfandikoo ay xayma: inference variational (fit distribution bu gëna yomb ci posterior dëgg), Markov chain Monte Carlo (parametru poid yi), wala pexe yu yomb yu melni Monte Carlo dropout ak test dropout ci reso bi. Payoff bi mooy kalibre incertitude - model bi xamna su input bi xamul (ci bitti distribution) te mën ko flag ci barabu wóolu guess.
Gis-gis xarala
Pexem Bayesian yi dañu wuutale ñaari mbir yu wóorul: aleatoric (bruit buñu mënul wàññi ci done yi) ak epistemik (njàqare gi ci model bi, te done yu bari mën nañu ko wàññi). Inference variational dafay reframes estimation bi ci ginaaw ni optimisation, di wàññi divergence KL ci digganté ab approximation ak ab posterior bu dëggu jaaraleko ci ELBO objectif. Benn yoon bu gàtt buñu mëna jëfandikoo, Monte Carlo dropout, dafay tekki dropout ni inference Bayesian bu jege: doxal reso bi N yoon dropout buy dox ak tasaaroo ci génne yi xayma ñàkka wóor epistemik.
njeextalu pexe
dogal yu gëna leer
Daf lay jàppale nga tàqale kàddu yu leer ci wàllu xarala ak làkku fësal njaay.
Njëgg ak budget
Mën nga laaj laaj yu gëna baax ci samp gi balaa ngay dugal xaalis wala sa jotu liggéey.
Ekip ak def liggéey
Ekip yi bokk xam-xam ñoo gëna mëna jël yenn dogal ci wàllu produit, politik ak jàng.
Ëlëgu jàng bu xóot bu Bayesian
Kom IA mungi duggu ci ay bërëb yu am solo ci wallu kaaraange, laaj xayma yu wóorul dara mungi yokku, di puus xalaati Bayesian yi ci gestu ci jëf. Xaarandi xayma yu gëna yomb (njëg li ci inference Bayesian bu mat ci echel mooy barier bi gëna mag), jëfandikoo ensembles yu xóot yu gëna yaatu ni stand-in pragmatic, ak boole ak model yu mag ngir wane hallucinations ak ay dugal yuñ xamul. Regulatër yi ci wàllu faju ak sistem yiy moom seen bopp dañu gëna bëgg wóolu seen bopp, moo tax jàngat bu xóot bi xam-xam bu wóorul nekk luy gëna am solo, du nekk lu ñuy xaar.
Doxal ci àdduna dëgg
Sistemu nataalu medsin yiy jox wóolu feebar bu nekk, ba noppi yónnee scanner yu wóorul yi ci radiologist nit.
Gis-gis biy dawal sa bopp dafay màndargaal mbir mu xamul ni lu wóorul dara moo tax oto bi dafay dawal ak moytu ludul wóolu boppam juum ci xaaj ko.
Gis ay dugal yu nekk ci bitti séddale ci sistemu njuuj njaaj wala kaaraange, fu ay done yu wuute ak yeneen yi wara jur ndànk moo gën jël dogal bu wóor.
Optimisation Bayesian dafay aju ci formulaasioŋu drog yi wala hyperparametre yiy jàng masin ci ekilibre seetlu gox yu wóorul ak gox yu baax yiñ xam.
Risk yi ak balustrade yi
Ekip yu bari mën nañu jëfandikoo benn baat ci anam wu wuute, kon teela leeral yaatuwaayam.
Benchmark yi mën nañu nuru lu am doole waaye performance yi ci àdduna bi duñu tolloo.
Bëgg kalite done ak palaŋu jàngat dafay faral di jur njariñ yu yomba dagg.
Roadmap ngir samp gi
Tàmbaleel ci joxe leeral ci làkk wu leer ci njariñ li nga soxla.
Tannal benn metric bu baax ak benn anam bu baaxul balaa ngay saytu.
Doxal ab pilote bu ndaw ak ay done yu representatif, du ab demo bu leer.
Bindal fi jàng bu xóot bu Bayesian di jàppale ak fi pexe yu gëna yomba gëna baax.
Weyal di banneexu
Free newsletter
Get the daily AI briefing
Three verified AI stories every weekday morning, written in plain English. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the Bayesian Deep Learning quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Gis bi ci topp
Jàng bu xóot
Laaj yi ñuy faral di laaj
What is Bayesian Deep Learning?
Jàngat bu xóot bu Bayesian dafay jàppee diisaayu reso neuronal bi ni distribution probabilite moo gën ni ay lim yu takku, suko defee model bi mën wax ni wóolu na. Loolu lu am solo la ci jëfandikoo yu am solo - medsin, oto yuy dawal seen bopp, xaalis - fu 'Yaakaaru ma' tontu bu am solo la.
Ak naka la reso neuronal bayesien di def ay poid yu wuute ak yi ñuy faral di def?
Reseau bayesien yi dañuy jàng distribution ci kaw poid yi, di jàpp lu wóorul ci seen valeur dëgg, ci barabu estimaasioŋ yu benn poñ.
Lan mooy njariñ li gëna mag ci jëfandikoo gis-gis bu Bayesian ci jàng bu xóot?
Suñu defee moyenne ci reso yu bari yu wóor, xeetu Bayesian yi mën nañu fësal wóolu seen bopp ba noppi màndargaal ay dugal yuñ xamul moo gën ñuy xalaat rek.
Lan mooy wuutale ñàkka wóor gu epistemik ak ñàkka wóor gu aleatorik?
Ñàkka wóorul ci wàllu xam-xam mingi aju ci ni model bi gisul ay done yu doy, ba noppi di wàññeeku su amee ay done yu bari; aleatoric uncertainty is irreducible noise in the data itself.
Lan moo tax ñu soxla xayma yu melni inferensi variasionel wala MCMC?
Xayma distribution posterior dëgg ci kaw poids yu bari yi ci reso bi mënu ñu ko def, kon ay pexe yu jege dañu koy xayma.
Lan la Monte Carlo def ngir xayma ñàkka wóor gi?
MC dropout dafay bàyyi dropout ci diiru inference ba noppi daw ay paas yu bari ci kanam; coppite yi am ci waxtaan yi dañuy jege ñàkka wóoru epistemik.