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Thomas Pfeil

2 accepted papers

2022

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

ICLR 2022spotlight

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…

2018

The streaming rollout of deep networks - towards fully model-parallel execution

NeurIPS 2018poster

Deep neural networks, and in particular recurrent networks, are promising candidates to control autonomous agents that interact in real-time with the physical world. However, this requires a seamless integration of temporal features into the network’s architecture. For the training of and inference…