Manuel Nonnenmacher

SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning

Pruning neural networks reduces inference time and memory costs. On standard hardware, these benefits will be especially prominent if coarse-grained structures, like feature maps, are pruned. We devise two novel saliency-based methods for second-order structured pruning (SOSP) which include correlat…