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Globally Induced Forest: A Prepruning Compression Scheme.

, , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 420-428. PMLR, (2017)

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Globally Induced Forest: A Prepruning Compression Scheme., , and . ICML, volume 70 of Proceedings of Machine Learning Research, page 420-428. PMLR, (2017)Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech., , , , , , and . CoRR, (2020)L1-based compression of random forest models., , , and . ESANN, (2012)CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech., , , , , and . INTERSPEECH, page 4387-4391. ISCA, (2020)Multi-Scale Spectrogram Modelling for Neural Text-to-Speech., , , , , , , , and . SSW, page 177-182. ISCA, (2021)Distribution Augmentation for Low-Resource Expressive Text-To-Speech., , , , , , , , , and 1 other author(s). ICASSP, page 8307-8311. IEEE, (2022)Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech., , , , , , and . ICASSP, page 6573-6577. IEEE, (2021)Controllable Emphasis with zero data for text-to-speech., , , , , , , , , and 4 other author(s). SSW, page 113-119. ISCA, (2023)CopyCat: Many-to-Many Fine-Grained Prosody Transfer for Neural Text-to-Speech., , , , , and . CoRR, (2020)Exploiting random projections and sparsity with random forests and gradient boosting methods - Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity.. University of Liège, Belgium, (2017)