Transfer learningAdversarial networkMixture of expertsLeveraging data from multiple related domains to enhance the model generalization performance is critical for transfer learning in text classification. Howe
With the advancement in deep learning, neural network architectures like recurrent neural networks (RNN and LSTM) and convolutional neural networks (CNN) have shown a decent improvement in performance in solving several Natural Language Processing (NLP) tasks like text classification, language model...
For more information about how to use the new SageMaker TensorFlow text classification algorithm for transfer learning on a custom dataset, deploy the fine-tuned model, run inference on the deployed model, and deploy the pre-trained model as is without first fine-tuning on a cu...
[Triplex Transfer Learning: Exploiting both Shared and Distinct Concepts for Text Classification] (intsci.ac.cn/users/zhua), Fuzhen Zhuang , Ping Luo, et al.,Matlab [Heterogeneous Transfer Learning for Image Classification] (cse.ust.hk/~yinz/htl4ic), Yin Zhu, Yuqiang Chen, et al.,Matlab [...
Transfer learning is a new machine learning and data mining framework that allows the training and test data to come from different distributions or feature spaces. We can find many novel applications of machine learning and data mining where transfer le
[Triplex Transfer Learning: Exploiting both Shared and Distinct Concepts for Text Classification] (...
Modern machine learning models, especially deep neural networks, can often benefit quite significantly from transfer learning. In computer vision, deep convolutional neural networks trained on a large image classification datasets such as ImageNet have proved to be useful for initializin...
Multi-source transfer learning has been explored widely in text classification33, pattern recognition in EEG signals34, speech recognition35 etc. One of the approaches for multi-source transfer learning relies on the assumption that the target task can be represented as a weighted combination of the...
Case I: Encoding Specfi c Knowledge for Feature Learning 仍然考虑前面 sentiment classification 的例子,我们希望使用很多标注好的电子产品评论数据来辅助训练电子游戏评论分类的任务。通常分类模型如下: 其中x 表示词库中每个词在该句子中出现的频率,w 表示每个词的情感评价,是基于数据回归得到的。 相应的迁移学习任...
Transfer Learning for Text Summarization Presented approaches Embedding named entities Embedding segment positions Adding copy-mechanism Sentence selection Masked in-domain pretraining (improved) Prerequisites The repository has been tested with Ubuntu 18.04 and CUDA 10.2. It requires Python 3.7, Anaconda, ...