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John Rachwan

5 papers hereh-index 349 citations6 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author3

Across the 5 of 5 papers where every author was matched, so the position is known.

fields
  • cs.LG4
  • cs.CV1

identity via Semantic Scholar / OpenAlex

activity
20222025
most citedWinning the Lottery Ahead of Time: Efficient Early Network Pruning

4 citations · 6 across the 5 of their papers we have counts for

collaborators
Showing 2024 · cs.LGShow all

1 paper · 2 filters

cs.LG2024

Structurally Prune Anything: Any Architecture, Any Framework, Any Time

Xun Wang, John Rachwan, Stephan Günnemann +1

Neural network pruning serves as a critical technique for enhancing the efficiency of deep learning models. Unlike unstructured pruning, which only sets specific parameters to zero…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.