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

, , , и . AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 1264-1274. PMLR, (2019)

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