Maswali yanayounganishwa na mwongozo · Kati Kiwango

Maswali ya Dhana ya Tikiti za Bahati Nasibu

Angalia uelewa wako wa tikiti za bahati nasibu, uanzishaji, upogoaji, na vikomo vya madai kuhusu mitandao midogo ya neva.

Njia za mwongozo zinazohusianaDhana ya Tikiti za Bahati Nasibu
Swali 1 ya 8

'Tiketi ya kushinda' ni nini katika dhana hii?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. 'Tiketi ya kushinda' ni nini katika dhana hii?
    • Mtandao mdogo mdogo ambao, umefunzwa kutoka kwa init yake asili, unalingana na mtandao kamili
    • Mtandao ulio na vigezo vingi kuliko kawaida
    • Muundo uliofunzwa awali uliopakuliwa kutoka kwa mtandao
    • Seti iliyokisiwa nasibu ya hyperparameta
  2. In the original lottery-ticket experiments, what was paired with the discovered sparse mask when retraining a ticket?
    • Fresh random values for every surviving weight
    • The surviving weights’ matched original initialization
    • A different architecture for each pruning round
    • The final trained weights with no resetting
  3. Nani alipendekeza Hypothesis ya Tikiti ya Bahati Nasibu?
    • Goodfellow (2014)
    • Krizhevsky, Sutskever, Hinton (2012)
    • Frankle na Carbin (2018)
    • Fedha na wenzake (2016)
  4. Which procedure did the original Lottery Ticket Hypothesis paper use to identify winning tickets?
    • Randomly remove a fixed half of all weights once
    • Iterative magnitude pruning with a reset to the original initialization
    • Add dropout after each pruning round
    • Increase the learning rate after every epoch
  5. How should the idea that overparameterization helps optimization find a trainable sparse structure be described?
    • A conjecture or proposed explanation, not a result established by the original experiments
    • A theorem proven for every neural-network architecture
    • A guarantee that all weights can be removed without loss
    • A claim that pruning eliminates the need for training data
  6. What does a strong lottery-ticket result claim in its theoretical setting?
    • Every randomly initialized network already solves any task
    • A sufficiently overparameterized randomly initialized network can contain a pruned subnetwork that approximates a target without further weight training
    • Pruning always preserves the trained model’s accuracy
    • A sparse network never needs data
  7. What does iterative magnitude pruning remove at each pruning step?
    • The training examples with the lowest labels
    • The neurons with the most outgoing connections
    • Weights with the smallest magnitudes
    • All weights in the final layer
  8. Which datasets were used in the original paper’s reported lottery-ticket experiments?
    • ImageNet and COCO only
    • LibriSpeech and Common Voice
    • SQuAD and GLUE
    • MNIST and CIFAR-10