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Found 2 papers, 1 papers with code
Compressing Pre-trained Language Models by Matrix Decomposition
Large pre-trained language models reach state-of-the-art results on many different NLP tasks when fine-tuned individually; They also come with a significant memory and computational requirements, calling for methods to reduce model sizes (green AI).
Transfer Learning Between Related Tasks Using Expected Label Proportions
We propose a novel application of the XR framework for transfer learning between related tasks, where knowing the labels of task A provides an estimation of the label proportion of task B.
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