Difference between revisions of "Text Transfer Learning"

Revision as of 13:56, 8 December 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. Colossal Clean Crawled Corpus — were sourced from the Common Crawl project, which scrapes roughly 20 terabytes of English text from the web each month.
Generative Pre-trained Transformer-2 (GPT-2)
Learning Techniques

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

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 * [[Generative Pre-trained Transformer-2 (GPT-2)]]
 * [[Learning Techniques]]
 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]