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Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options.

, , , , , and . EMBC, page 969-972. IEEE, (2011)

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Deep Learning Training in Facebook Data Centers: Design of Scale-up and Scale-out Systems., , , , , , , , , and 5 other author(s). CoRR, (2020)Scalable Distributed Training of Recommendation Models: An ASTRA-SIM + NS3 case-study with TCP/IP transport., , , , , , , and . Hot Interconnects, page 33-42. IEEE, (2020)Supporting Massive DLRM Inference Through Software Defined Memory., , , , , , , , , and 10 other author(s). CoRR, (2021)Morphologically constrained signal subspace characterization of electrograms during ventricular fibrillation., , , , , , , , and . Biomed. Signal Process. Control., (2017)Check-N-Run: A Checkpointing System for Training Recommendation Models., , , , , , , and . CoRR, (2020)Wavelet-based features for characterizing ventricular arrhythmias in optimizing treatment options., , , , , and . EMBC, page 969-972. IEEE, (2011)Instantaneous Time-Frequency Features in Characterizing Ventricular Arrhythmias Using Empirical Mode Decomposition., , , , , and . ACSSC, page 1225-1229. IEEE, (2018)XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse., , , , , , , , , and 8 other author(s). CoRR, (2022)An In-Silico model for evaluating the directional shock vectors in terminating and modulating rotors., , , , , , and . Comput. Biol. Medicine, (2022)MTIA: First Generation Silicon Targeting Meta's Recommendation Systems., , , , , , , , , and 46 other author(s). ISCA, page 80:1-80:13. ACM, (2023)