Revision as of 09:54, 17 April 2023 edit Citation bot (talk \| contribs) Bots 5,865,963 edits Add: authors 1-1. Removed proxy/dead URL that duplicated identifier. Removed parameters. Some additions/deletions were parameter name changes. \| Use this bot. Report bugs. \| Suggested by Spinixster \| Category:Artificial neural networks \| #UCB_Category 59/146 ← Previous edit		Revision as of 18:04, 1 May 2023 edit undo JwixVonJwax (talk \| contribs) 11 edits m Fixes typo Next edit →
Line 9: #Remove the least important neuron. #Check a termination condition (to be determined by the user) to see whether to continue pruning. Recently a highly ~~pruning~~pruned three layer tree architecture, has achieved a similar success rate to that of LeNet-5 on the CIFAR-10 dataset with a lesser computational complexity.<ref>{{Cite journal \|last1=Meir \|first1=Yuval \|last2=Ben-Noam \|first2=Itamar \|last3=Tzach \|first3=Yarden \|last4=Hodassman \|first4=Shiri \|last5=Kanter \|first5=Ido \|date=2023-01-30 \|title=Learning on tree architectures outperforms a convolutional feedforward network \|journal=Scientific Reports \|language=en \|volume=13 \|issue=1 \|pages=962 \|doi=10.1038/s41598-023-27986-6 \|issn=2045-2322 \|pmc=9886946 \|pmid=36717568}}</ref> == References ==

Pruning (artificial neural network): Difference between revisions