Kediska mala-awaalka tigidhada bakhtiyaa nasiibka

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

1. Waa maxay tigidhka guusha' ee mala-awaalkan?
   • Shabakad hoose oo kooban oo, laga soo tababaray asalkeedii hore, oo ku habboon shabakada buuxda
   • Shabakad leh cabbirro ka badan inta caadiga ah
   • Nooc hore loo tababaray oo laga soo dejiyay internetka
   • Qalabka hyperparameters oo si aan kala sooc lahayn loo qiyaasay
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. Yaa soo jeediyay mala-awaalka tigidhka bakhtiyaanasiibka?
   • Goodfellow (2014)
   • Krizhevsky, Sutskever, Hinton (2012)
   • Frankle iyo Carbin (2018)
   • Silver iyo asxaabta (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