Difference between revisions of "Text Transfer Learning"
| Line 4: | Line 4: | ||
* [http://pdfs.semanticscholar.org/1bb2/39731589f3114a3fe5b35e42a635b5eacb38.pdf Transfer Learning for Text Mining | Weike Pan, Erheng Zhong, and Qiang Yang] | * [http://pdfs.semanticscholar.org/1bb2/39731589f3114a3fe5b35e42a635b5eacb38.pdf Transfer Learning for Text Mining | Weike Pan, Erheng Zhong, and Qiang Yang] | ||
* [[Natural Language Processing (NLP)]] | * [[Natural Language Processing (NLP)]] | ||
| − | + | * [http://venturebeat.com/2019/10/24/google-achieves-state-of-the-art-nlp-performance-with-an-enormous-language-model-and-data-set/ Google achieves state-of-the-art NLP performance with an enormous language model and data set | Kyle Wiggers - Venture Beat] researchers at Google developed a new data set — Colossal Clean Crawled Corpus — and a unified framework and model dubbed [http://arxiv.org/pdf/1910.10683.pdf Text-to-Text Transformer] that converts language problems into a text-to-text format. | |
Transfer algorithms: Bi-Directional Attention Flow (BIDAF), Document-QA (DOCQA), Reasoning Network (ReasoNet), R-NET, S-NET, and Assertion Based Question Answering (ABQA) [http://blogs.technet.microsoft.com/machinelearning/2018/04/25/transfer-learning-for-text-using-deep-learning-virtual-machine-dlvm/ Transfer Learning for Text using Deep Learning Virtual Machine (DLVM) | Anusua Trivedi and Wee Hyong Tok - Microsoft] | Transfer algorithms: Bi-Directional Attention Flow (BIDAF), Document-QA (DOCQA), Reasoning Network (ReasoNet), R-NET, S-NET, and Assertion Based Question Answering (ABQA) [http://blogs.technet.microsoft.com/machinelearning/2018/04/25/transfer-learning-for-text-using-deep-learning-virtual-machine-dlvm/ Transfer Learning for Text using Deep Learning Virtual Machine (DLVM) | Anusua Trivedi and Wee Hyong Tok - Microsoft] | ||
Revision as of 21:47, 25 October 2019
- Image/Video Transfer Learning
- Transfer Learning for Text Mining | Weike Pan, Erheng Zhong, and Qiang Yang
- Natural Language Processing (NLP)
- Google achieves state-of-the-art NLP performance with an enormous language model and data set | Kyle Wiggers - Venture Beat researchers at Google developed a new data set — Colossal Clean Crawled Corpus — and a unified framework and model dubbed Text-to-Text Transformer that converts language problems into a text-to-text format.
Transfer algorithms: Bi-Directional Attention Flow (BIDAF), Document-QA (DOCQA), Reasoning Network (ReasoNet), R-NET, S-NET, and Assertion Based Question Answering (ABQA) Transfer Learning for Text using Deep Learning Virtual Machine (DLVM) | Anusua Trivedi and Wee Hyong Tok - Microsoft
