Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Ohunkikan Architecture Search adanwo

Review NAS search spaces, algorithms, early search cost, DARTS relaxation, weight sharing, and evaluation caveats. Check assumptions and scope.

Jẹmọ itọsọna awọn ọnaOhunkikan Architecture Search
Ibeere 1 ti 8

Kini Wiwa Architecture Neural ṣe adaṣe?

Awotẹlẹ gbogbo ibeere8 ibeere

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

  1. Kini Wiwa Architecture Neural ṣe adaṣe?
    • Awọn oniru ti awọn nkankikan nẹtiwọki ká be
    • Ifi aami ikẹkọ data
    • Itutu awọn olupin GPU
    • Awọn iwe kikọ fun awọn awoṣe
  2. Eyi ti mẹta irinše asọye a NAS ọna?
    • Iṣeto data, iṣapeye, ati yiyọ kuro
    • Aaye wiwa, ilana wiwa, ati iṣiro iṣẹ ṣiṣe
    • Encoder, decoder, ati akiyesi
    • Ikẹkọ, afọwọsi, ati awọn eto idanwo
  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. Ẹtan bọtini wo ni DARTS nlo lati jẹ ki wiwa daradara?
    • O ṣe ikẹkọ lori apẹẹrẹ kan
    • O yọ gbogbo awọn ipele ti o farapamọ kuro
    • O nlo wiwa laileto nikan
    • O sinmi awọn yiyan faaji ọtọtọ sinu awọn ti o tẹsiwaju ti o dara julọ nipasẹ awọn gradients
  5. Kini 'supernet' ni NAS pinpin iwuwo?
    • Nẹtiwọọki ti o ju-parameterized kan ti o ni gbogbo awọn ayaworan ile oludije, ti awọn iwuwo wọn pin
    • Nẹtiwọọki pẹlu awọn ọkẹ àìmọye ti awọn paramita
    • Nẹtiwọọki nṣiṣẹ lori supercomputers nikan
    • A daakọ afẹyinti ti awoṣe
  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