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Techniques for Improving Filters in Power Grid Contingency Analysis.

, , , , and . MLDM, volume 6871 of Lecture Notes in Computer Science, page 599-611. Springer, (2011)

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Weightless: Lossy Weight Encoding For Deep Neural Network Compression., , , , , , and . CoRR, (2017)Deep Learning for Computer Architects, , , , and . Synthesis Lectures on Computer Architecture Morgan & Claypool Publishers, (2017)Fathom: reference workloads for modern deep learning methods., , , , and . IISWC, page 148-157. IEEE Computer Society, (2016)Cognitive Computing Safety: The New Horizon for Reliability / The Design and Evolution of Deep Learning Workloads., , , , , , and . IEEE Micro, 37 (1): 15-21 (2017)Scalable, Multithreaded, Partially-in-Place Sorting., , and . IPDPS Workshops, page 1656-1664. IEEE, (2013)Characterizing the Scalability of Graph Convolutional Networks on Intel® PIUMA., , , , , , , , , and 2 other author(s). ISPASS, page 168-177. IEEE, (2023)MachSuite: Benchmarks for accelerator design and customized architectures., , , , and . IISWC, page 110-119. IEEE Computer Society, (2014)Towards Efficient N-x Contingency Selection Using Group betweenness Centrality., , , , , and . SC Companion, page 273-282. IEEE Computer Society, (2012)Weightless: Lossy weight encoding for deep neural network compression., , , , , , and . ICLR (Workshop), OpenReview.net, (2018)Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators., , , , , , , , and . ISCA, page 267-278. IEEE Computer Society, (2016)