Prueba de hipótesis sobre billetes de lotería

Las respuestas y explicaciones aparecen a medida que realiza el cuestionario.

1. ¿Qué es un "boleto ganador" en esta hipótesis?
   • Una subred dispersa que, entrenada desde su inicio original, coincide con la red completa
   • Una red con más parámetros de lo habitual
   • Un modelo previamente entrenado descargado de Internet.
   • Un conjunto de hiperparámetros adivinados aleatoriamente
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. ¿Quién propuso la hipótesis del billete de lotería?
   • Buen compañero (2014)
   • Krizhevsky, Sutskever, Hinton (2012)
   • Frankle y Carbin (2018)
   • Plata y colegas (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