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It is challenging to obtain extensive annotated data for under-resourced languages, so we investigate whether it is beneficial to train models using multi-
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Bayesian multitask MKL involves the selection of pri- ors and different selection of priors can lead to a dra- matically different result. MKL was also implemented to predict survival at 2000 days from diagnosis, using the METABRIC dataset for breast cancer, and observed that predictive accuracy ...
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Learning General Purpose Distributed Sentence Representations via Large Scale Multi-task Learning. Sandeep Subramanian Adam Trischler Yoshua Bengio Christopher J. Pal 原文链接 谷歌学术 必应学术 百度学术 All-but-the-Top: Simple and Effective Postprocessing for Word Representations. ...
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The overall original task performance is presented in Fig. 1c. We find that preservation of the original data allows for better model fine-tuning and leads to only minor degradation of performance. Both training regimes lead to degraded performance in our models, yet we do find that learning ...
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