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Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation.

, , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1264-1274. PMLR, (2019)

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Low-Precision Random Fourier Features for Memory-Constrained Kernel Approximation., , , and . CoRR, (2018)Filter & follow: how social media foster content curation., , , and . SIGMETRICS, page 43-55. ACM, (2014)Compact kernel models for acoustic modeling via random feature selection., , , and . ICASSP, page 2424-2428. IEEE, (2016)Low-Precision Random Fourier Features for Memory-constrained Kernel Approximation., , , and . AISTATS, volume 89 of Proceedings of Machine Learning Research, page 1264-1274. PMLR, (2019)Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding., , , , , , and . CoRR, (2024)Contextual Embeddings: When Are They Worth It?, , , and . ACL, page 2650-2663. Association for Computational Linguistics, (2020)Kernel Approximation Methods for Speech Recognition.. Columbia University, USA, (2018)Understanding the Downstream Instability of Word Embeddings., , , , , and . MLSys, mlsys.org, (2020)Contextual Embeddings: When Are They Worth It?, , , and . Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, page 2650--2663. Online, Association for Computational Linguistics, (July 2020)Kernel Approximation Methods for Speech Recognition., , , , , , , , , and 2 other author(s). J. Mach. Learn. Res., (2019)