Most Recommended Negative Results

What happens to the experiments which don’t show improvement over our previous baseline? In the data science / machine learning community, we hear that negative results can bring balance and objectivity. Yet there is still a publication bias, and a lack of sources for great negative results content. Here I’ve selected three papers which are recognized as standout examples of negative results, with added commentary or definitions.

If this interests you, I’d also recommend the 2020 and upcoming 2021 EMNLP Insights workshop which specifically calls for papers about negative results.

Paper One

  • identifying situations where batch normalization causes issues
  • describing alternatives to replace the benefits of batch normalization

Paper Two

Paper Three

YOLOv3 draws bounding boxes over objects; Detectron is a similar project which outlines objects


This article was posted in June 2021. For my latest recommendations, check this GitHub Readme.



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