Binciken gine-ginen Neural
Neural Architecture Search (NAS) yana sarrafa ƙira na tsarin hanyar sadarwa na jijiyoyi - barin algorithms, ba mutane ba, yanke hukunci nawa yadudduka, menene ayyuka, da yadda suke haɗawa.
Dubawa
It turns model design into a search problem, discovering architectures that can rival or beat hand-crafted ones.
Zurfafa nutsewa
Zayyana hanyoyin sadarwar jijiyoyi da hannu yana jinkirin kuma ya dogara da basirar ƙwararru. NAS ta maye gurbin hakan tare da bincike kan ƙayyadaddun sarari na yuwuwar gine-gine, jagorar dabarar da ke ba da shawarar ƴan takara da hanyar ƙididdige yadda kowannensu yake da kyau. NAS na farko ya yi amfani da koyo na ƙarfafawa ko algorithms na juyin halitta, horar da dubban hanyoyin sadarwar ɗan takara - wanda ya shahara da tsadar dubban kwanakin GPU. Ci gaban binciken ya kasance mai rahusa: rabon nauyi ('supernet' wanda ya ƙunshi duk 'yan takara) da kuma hanyoyi daban-daban kamar DARTS, waɗanda ke shakata da zaɓaɓɓun zaɓi cikin masu ci gaba don haka zuriyar gradient na iya haɓaka gine-gine da nauyi tare. NAS ta samar da ingantattun samfura irin su EfficientNet da yawancin hanyoyin sadarwa na wayar hannu da ake amfani da su a samarwa.
Fahimtar Fasaha
NAS tana da abubuwa uku: sararin bincike (tubalan gini da yadda zasu iya haɗawa), dabarun nema (ƙarfafa koyo, juyin halitta, binciken bazuwar, ko tushen gradient), da hanyar kimanta aiki. Horar da kowane ɗan takara zuwa haɗin kai yana da tsada mai tsada, don haka NAS tana amfani da gajerun hanyoyi: raba nauyi a cikin babban abin dogaro, ƙarancin aminci (ƙaɗan zamani, ƙananan bayanai), da kuma masu hasashen koyo. DARTS yana yin zaɓin zaɓi na 'wanne aiki ke zuwa nan' ci gaba ta hanyar gaurayawan masu nauyi mai nauyi, yana haɓakawa tare da gradients, sannan ya ɓarna sakamakon zuwa gine-gine na ƙarshe.
Dabarun Tasiri
Shawarwari masu haske
Yana taimaka muku keɓance bayyanannen da'awar fasaha daga harshen talla.
Kudin da kasafin kuɗi
Kuna iya yin mafi kyawun tambayoyin aiwatarwa kafin kashe kuɗi ko lokaci.
Ƙungiya da aikin aiki
Ƙungiyoyin da ke da fahimtar juna suna yin mafi kyawun samfura, manufofi, da yanke shawara na koyo.
Makomar Binciken Gine-ginen Jijiya
NAS yana faɗaɗawa daga daidaito-kawai maƙasudi zuwa masaniyar kayan masarufi, bincike-bincike da yawa waɗanda ke haɓaka latency, kuzari, da ƙwaƙwalwar ajiya don takamaiman guntu-mahimmanci ga gefuna da AI ta hannu. Sifili-farashin wakilai waɗanda ke ba da darajar gine-gine ba tare da horarwa ba suna saurin bincike sosai. Kamar yadda masu canji suka mamaye, NAS ana amfani da su ga tsarin kulawa, faɗin layi, da duk saitunan LLM, kuma yana haɗuwa tare da bututun koyon injin sarrafa kansa. Iyakar tana tsara samfura da kayan aiki tare, tare da madaukai na bincike waɗanda suka dace da ƙayyadaddun ƙaddamarwa ta atomatik.
Aiwatar da Gaskiyar Duniya
Iyalin EfficientNet Google, wanda tsarin gine-ginen da aka sikensa ya gudana ta hanyar bincike mai sarrafa kansa don samun daidaito-kowace-FLOP.
Samfuran hangen nesa ta wayar hannu (kamar MnasNet) an bincika tare da latency akan ainihin wayar a cikin madauki don saurin kan na'urar.
Hardware-sani NAS wanda ke keɓance hanyar sadarwa zuwa ƙayyadaddun ƙwaƙwalwar ajiyar gaggawa da ƙididdige iyaka.
Kafofin watsa labaru na AutoML waɗanda ke barin waɗanda ba ƙwararru ba su sami ƙirar ƙira ta al'ada ta hanyar bincika gine-gine ta atomatik.
Hatsari & Tsare-tsare
Ƙungiyoyi daban-daban na iya amfani da kalmar iri ɗaya daban, don haka ayyana iyaka da wuri.
Alamomi na iya yin kama da ƙarfi yayin da aikin zahirin duniya bai yi daidai ba.
Yin watsi da ingancin bayanai da tsare-tsaren kimantawa galibi yana haifar da sakamako mara ƙarfi.
Taswirar Hanya
Fara da ma'anar harshe a sarari na sakamakon da kuke buƙata.
Zaɓi ma'aunin nasara ɗaya da yanayin gazawa ɗaya kafin gwaji.
Gudun ƙaramin matukin jirgi tare da bayanan wakilci, ba saitin demo da aka goge ba.
Daftarin aiki inda Neural Architecture Search ke taimakawa kuma inda mafi sauƙi hanyoyin suka fi kyau.
Ci gaba da Bincike
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 Neural Architecture Search 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
Jagora na gaba
Graph Neural Networks
Tambayoyin da ake yawan yi
What is Neural Architecture Search?
Neural Architecture Search (NAS) yana sarrafa ƙira na tsarin hanyar sadarwa na jijiyoyi - barin algorithms, ba mutane ba, yanke hukunci nawa yadudduka, menene ayyuka, da yadda suke haɗawa. Yana juya ƙirar ƙira zuwa matsalar bincike, gano gine-ginen gine-ginen da za su iya yin hamayya ko doke waɗanda aka yi da hannu.
Menene Neural Architecture Search ke sarrafa kansa?
NAS tana sarrafa zabar yadudduka, ayyuka, da haɗin kai - gine-ginen kanta - maimakon dogaro da ƙirar ɗan adam kawai.
Wadanne sassa uku ne suka ayyana hanyar NAS?
An tsara NAS azaman sararin bincike, dabarun bincike don gano shi, da kuma hanyar kimanta aikin kowane ɗan takara.
Me yasa aka soki tushen ƙarfafawa-da farko NAS?
Horar da dubban hanyoyin sadarwar 'yan takara da aka yi da wuri NAS masu tsada sosai wajen ƙididdigewa, ƙarfafa hanyoyin masu rahusa.
Wace dabara ce DARTS ke amfani da ita don inganta bincike?
DARTS (Binciken Gine-gine na Daban-daban) yana juya zaɓen aiki mai mahimmanci zuwa ci gaba, cakuda mai nauyi mai laushi don haka zuriyar gradient ya shafi.
Menene 'supernet' a cikin rabon nauyi NAS?
Supernet ya ƙunshi kowane gine-ginen ɗan takara kuma yana raba ma'auni a tsakanin su, don haka 'yan takara ba sa buƙatar horar da su daga karce.