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bguedj-talk-2021-chalmers.pdf
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🔗 Source: bguedj.github.io
📊 Size: 3.7 MB
👤 Author: Benjamin Guedj ` `%%%`#`&12_`__~~~ alse 55 @bguedj
⬇️ Downloads: 237

Summary

Benjamin Guedj discusses generalisation and learning, specifically PAC-Bayes, with a background in pure mathematics and statistics. His research focuses on statistical learning theory, computational statistics, and deep learning, with a personal obsession with generalisation. He notes that learning is about generalising from examples to underlying phenomena, and that memorising seen data can lead to overfitting. He also touches on the apparent paradox of deep learning achieving low test errors despite overfitting on training data.

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Description

Benjamin Guedj discusses generalisation and learning, specifically PAC-Bayes, with a background in pure mathematics and statistics.

Technical Information

  • File Format: PDF
  • File Size: 3.7 MB
  • Pages: 39
  • Language: EN
  • Author: Benjamin Guedj ` `%%%`#`&12_`__~~~ alse 55 @bguedj
  • Total Downloads: 237
  • Last Updated: 3 weeks ago

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