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The video is reposted for educational purposes and encourages involvement in the field of research.
Source: https://icml.cc/virtual/2020/tutorial/5751
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Abstract:
Text simplification (TS) is a monolingual text-to-text transformation task where an original (complex) text is trans-formed into a target (simpler) text. Most recent work is based on sequence-to-sequence neural models similar to those used for machine translation (MT). Different from MT, TS data comprises more elaborate transformations, such as sentence splitting. It can also contain multiple simplifications of the same original text targeting different audiences, such as school grade levels. We explore these two features of TS to build models tailored for specific grade levels. Our approach uses a standard sequence-to-sequence architecture where the original sequence is annotated with information about the target audience and/or the (predicted) type of simplification operation. We show that it outperforms state-of-the-art TS approaches (up to 3 and 12 BLEU and SARI points, respectively), including when training data for the specific complex-simple combination of grade levels is not available, i.e. zero-shot learning.
...
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Source: https://youtu.be/7Rb4s9wNOmc
Course website: http://bit.ly/pDL-home
Playlist: http://bit.ly/pDL-YouTube
Week 8: http://bit.ly/pDL-en-08
0:00:00 – Week 8 – Practicum
PRACTICUM: http://bit.ly/pDL-en-08-3
In this section, we discussed a specific type of generative model called Variational Autoencoders and compared their functionalities and advantages over Classic Autoencoders. We explored the objective function of VAE in detail, understanding how it enforced some structure in the latent space. Finally, we implemented and trained a VAE on the MNIST dataset and used it to generate new samples.
0:02:35 – Autoencoders (AEs) vs. variational autoencoders (VAEs)
0:16:37 – Understanding the VAE objective function
0:31:33 – Notebook example for variational autoencoder
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The video was published under the license of the Creative Commons Attribution license (reuse allowed). It is reposted for educational purposes and encourages involvement in the field of research.
Source: https://vimeo.com/439776761
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The video was published under the license of the Creative Commons Attribution license (reuse allowed). It is reposted for educational purposes and encourages involvement in the field of research.
Source: https://youtu.be/RGFcDlX7ulM
Subscribe to Andreas Maier: https://www.youtube.com/channel/UCoiMqX5FHfk_KDow7xSe7pg
...
https://www.youtube.com/watch?v=khX_VGqtUF4
Welcome to AIP.
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Source: https://www.youtube.com/watch?v=0ZVOmBp29E0
In this research meeting, guest Stephen Omohundro gave a fascinating talk on GPT-3, the new massive OpenAI Natural Language Processing model. He reviewed the network architecture, training process, and results in the context of past work. There was extensive discussion on the implications for NLP and for Machine Intelligence / AGI.
Link to GPT-3 paper: https://arxiv.org/abs/2005.14165
Link to slides from this presentation: https://www.slideshare.net/numenta/openais-gpt-3-language-model-guest-steve-omohundro
...
https://www.youtube.com/watch?v=FXlpPdmYEnY