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First publicized online: january 10, 2018 abstract this study attempts to provide an general characterization of youtube, based on letter a random sample of channel and picture data, by exhibit how video planning and consumption evolved over the of course of the former 10 years.
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What's the core idea of this research paper?
Their method, called Universal Language Model Fine-Tuning (ULMFiT) outperforms state-of-the-art results, reducing the error by 18-24%. Even more, with only 100 labeled examples, ULMFiT matches the performance of models trained from scratch on 10K labeled examples. What’s the core idea of this paper?
Which is the Best AI research paper of 2018?
If you’d like to skip around, here are the papers we featured: 1. Universal Language Model Fine-tuning for Text Classification, by Jeremy Howard and Sebastian Ruder (2018) Inductive transfer learning has greatly impacted computer vision, but existing approaches in NLP still require task-specific modifications and training from scratch.
Last Update: Oct 2021
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Kandida
22.10.2021 03:34
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