Statements (30)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
scientific theory
|
| gptkbp:author |
Jonathan Frankle, Michael Carbin
|
| gptkbp:citation |
gptkb:ICLR_2019
gptkb:The_Lottery_Ticket_Hypothesis:_Finding_Sparse,_Trainable_Neural_Networks 2019 |
| gptkbp:debatedBy |
applicability to transfer learning
effectiveness in different architectures generalizability to large-scale models |
| gptkbp:field |
gptkb:machine_learning
|
| gptkbp:hasConcept |
Dense, randomly-initialized neural networks contain subnetworks that can be trained in isolation to reach comparable accuracy to the original network.
winning ticket |
| https://www.w3.org/2000/01/rdf-schema#label |
Lottery Ticket Hypothesis
|
| gptkbp:influenced |
network initialization studies
pruning algorithms research on efficient neural networks |
| gptkbp:method |
Iterative pruning and retraining
|
| gptkbp:proposedBy |
gptkb:Jonathan_Frankle
gptkb:Michael_Carbin |
| gptkbp:publishedIn |
gptkb:International_Conference_on_Learning_Representations_(ICLR)
|
| gptkbp:relatedTo |
deep learning
model compression neural network pruning |
| gptkbp:testedBy |
gptkb:ImageNet_dataset
gptkb:MNIST_dataset gptkb:CIFAR-10_dataset image classification tasks |
| gptkbp:winningTicketDefinition |
A sparse subnetwork that can be trained from its original initialization to match the accuracy of the full network.
|
| gptkbp:yearProposed |
2018
|
| gptkbp:bfsParent |
gptkb:Jonathan_Frankle
|
| gptkbp:bfsLayer |
7
|