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UID:pretalx-euroscipy-2022-E7Z3VY@pretalx.com
DTSTART;TZID=CET:20220829T083000
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DESCRIPTION:Every scientific conference has seen a massive uptick in applic
 ations that use some type of machine learning. Whether it’s a linear reg
 ression using scikit-learn\, a transformer from Hugging Face\, or a custom
  convolutional neural network in Jax\, the breadth of applications is as v
 ast as the quality of contributions.\n\nThis tutorial aims to provide easy
  ways to increase the quality of scientific contributions that use machine
  learning methods. The reproducible aspect will make it easy for fellow re
 searchers to use and iterate on a publication\, increasing citations of pu
 blished work. The use of appropriate validation techniques and increase in
  code quality accelerates the review process during publication and avoids
  possible rejection due to deficiencies in the methodology. Making models\
 , code and possibly data available increases the visibility of work and en
 ables easier collaboration on future work.\n\nThis work to make machine le
 arning applications reproducible has an outsized impact compared to the li
 mited additional work that is required using existing Python libraries.
DTSTAMP:20260309T094350Z
LOCATION:HS 120
SUMMARY:Increase citations\, ease review & collaboration – Making machine
  learning in research reproducible - Jesper Dramsch
URL:https://pretalx.com/euroscipy-2022/talk/E7Z3VY/
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