Machine Learning Summer School for 2020
Published:
This page aims to list all of the observed machine learning summer schools in 2020 with the detail time, locations, and application deadline Read more
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Learning
Machine Learning
Employ Transfer Learning Approach for Sentiment Classification at TenPoint7
Published:
Universal Language Model Fine-tuning (ULMFiT)1 is an effective transfer learning approach for solving variety tasks in Natural Language Processing (NLP). The authors shed some light on the three common tasks of text classification: sentiment analysis, question classification, and topic classification. Their work especially achieves state-of-the-art results on six text classification tasks.
- Jeremy Howard and Sebastian Ruder, Universal Language Model Fine-tuning for Text Classification, 2018 ↩
Machine Learning Summer School for 2020
Published:
This page aims to list all of the observed machine learning summer schools in 2020 with the detail time, locations, and application deadline Read more
Natural Language Processing
Employ Transfer Learning Approach for Sentiment Classification at TenPoint7
Published:
Universal Language Model Fine-tuning (ULMFiT)1 is an effective transfer learning approach for solving variety tasks in Natural Language Processing (NLP). The authors shed some light on the three common tasks of text classification: sentiment analysis, question classification, and topic classification. Their work especially achieves state-of-the-art results on six text classification tasks.
- Jeremy Howard and Sebastian Ruder, Universal Language Model Fine-tuning for Text Classification, 2018 ↩
Summer School
Machine Learning Summer School for 2020
Published:
This page aims to list all of the observed machine learning summer schools in 2020 with the detail time, locations, and application deadline Read more
Transfer Learning
Employ Transfer Learning Approach for Sentiment Classification at TenPoint7
Published:
Universal Language Model Fine-tuning (ULMFiT)1 is an effective transfer learning approach for solving variety tasks in Natural Language Processing (NLP). The authors shed some light on the three common tasks of text classification: sentiment analysis, question classification, and topic classification. Their work especially achieves state-of-the-art results on six text classification tasks.
- Jeremy Howard and Sebastian Ruder, Universal Language Model Fine-tuning for Text Classification, 2018 ↩