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Titlebook: Deep Learning Applications, Volume 3; M. Arif Wani,Bhiksha Raj,Dejing Dou Book 2022 The Editor(s) (if applicable) and The Author(s), under

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發(fā)表于 2025-3-23 12:10:49 | 只看該作者
Walid Larbi,Jean-Fran?ois Deü,Roger Ohayonhic Forgetting. Across multiple experiments and datasets, we show that Deep RCA achieves the optimal joint training performance when sequentially trained on new tasks using just the new task data. It does this by utilizing a modified recursive least squares algorithm and a novel recursive null-class
12#
發(fā)表于 2025-3-23 14:18:11 | 只看該作者
Walid Larbi,Jean-Fran?ois Deü,Roger Ohayonndard approach for many languages. However, an in-depth understanding of the effect of using these models is still missing for less spoken languages. This study gives a comprehensive analysis of using the BERT model for languages with rich morphology. We experimented with cross-lingual, multilingual
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發(fā)表于 2025-3-23 22:04:55 | 只看該作者
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發(fā)表于 2025-3-23 23:00:59 | 只看該作者
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發(fā)表于 2025-3-24 07:26:26 | 只看該作者
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發(fā)表于 2025-3-24 12:00:48 | 只看該作者
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發(fā)表于 2025-3-24 18:46:46 | 只看該作者
Deep Learning Applications, Volume 3978-981-16-3357-7Series ISSN 2194-5357 Series E-ISSN 2194-5365
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發(fā)表于 2025-3-24 21:39:05 | 只看該作者
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發(fā)表于 2025-3-25 00:12:08 | 只看該作者
A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology,ndard approach for many languages. However, an in-depth understanding of the effect of using these models is still missing for less spoken languages. This study gives a comprehensive analysis of using the BERT model for languages with rich morphology. We experimented with cross-lingual, multilingual
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