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Multiplying Matrices Without Multiplying [article]

Davis Blalock, John Guttag
<span title="2021-06-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
These results suggest that a mixture of hashing, averaging, and byte shuffling-the core operations of our method-could be a more promising building block for machine learning than the sparsified, factorized  ...  Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Consequently, there has been significant work on efficiently approximating matrix multiplies.  ...  ., and Sze, V. (eds.), Proceedings of Machine Learning and Systems Drineas, P., Kannan, R., and Mahoney, M. W. Fast 2020, MLSys 2020, Austin, TX, USA, March 2-4, 2020.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.10860v1">arXiv:2106.10860v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uny4gzjt4reblemp5ectkmccmi">fatcat:uny4gzjt4reblemp5ectkmccmi</a> </span>
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