Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Raadinta Dhismaha Neural

Dib u eeg meelaha NAS raadinta, algorithms, qiimaha raadinta hore, nasashada DARTS, miisaanka qaybsiga, iyo digniinaha qiimaynta. Hubi fikradaha iyo baaxadda.

Wadooyinka haga ee la xidhiidhaRaadinta Dhismaha Neural
Su'aal 1 ee 8

Muxuu Raadiyaha Neural Architecture si otomaatig ah u sameeyaa?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Muxuu Raadiyaha Neural Architecture si otomaatig ah u sameeyaa?
    • Naqshadaynta qaab dhismeedka shabakada neerfaha
    • Sumadeynta xogta tababarka
    • Qaboojinta adeegayaasha GPU-da
    • Qorista dukumeentiyada moodooyinka
  2. Saddexdee qaybood ayaa qeexaya habka NAS?
    • Dataset, optimizer, and dropout
    • Meel ka raadi, istaraatijiyad raadin, iyo qiimaynta waxqabadka
    • Codeeyaha, codeeyaha, iyo feejignaanta
    • Tababarka, ansixinta, iyo qaybaha imtixaanada
  3. What accelerator scale did the original reinforcement-learning NAS paper report for its large search?
    • One GPU for every NAS method
    • A fixed cost of exactly 1,000 GPU-days for every search
    • An experiment using 800 GPUs in that study
    • No accelerator use during architecture search
  4. Waa maxay khiyaamada muhiimka ah ee DARTS ay isticmaasho si ay u raadiso mid hufan?
    • Waxay ku tababartaa hal tusaale
    • Waxay ka saartaa dhammaan lakabyada qarsoon
    • Waxa ay isticmaashaa kaliya raadinta random
    • Waxay dejinaysaa xulashooyinka qaab dhismeedka kala duwan oo u beddela kuwo joogto ah oo ay hagaajin karaan gradients
  5. Waa maxay 'supernet' ee miisaan-qaybsiga NAS?
    • Hal shabakad oo aad loo qiyaaso oo ka kooban dhammaan qaab-dhismeedka musharraxiinta, kuwaas oo miisaankooda la wadaago
    • Shabakad leh balaayiin cabbir
    • Shabakad ku shaqaysa supercomputer kaliya
    • Nuqul kayd ah ee moodeelka
  6. What did the HURRICANE hardware-aware NAS study evaluate?
    • One architecture that is guaranteed optimal on every device
    • Tailored architectures for three hardware types under matched latency constraints
    • A language model trained without any hardware measurements
    • A method that removes latency from architecture evaluation
  7. What is one limitation of weight-sharing NAS supernets?
    • They remove all architecture choices
    • They guarantee the globally best architecture
    • The shared weights can bias architecture rankings relative to training each candidate independently
    • They do not use neural networks
  8. What does a NAS evaluation need to report for a fair comparison?
    • Only the best test score
    • The GPU brand alone
    • The model name without settings
    • Search space, compute budget, validation protocol, and final retraining setup