Troubling Trends In Machine Learning Scholarship
Published August 6, 2018
|
29 min
    Download
    Add to queue
    Copy URL
    Show notes
    There's a lot of great machine learning papers coming out every day--and, if we're being honest, some papers that are not as great as we'd wish. In some ways this is symptomatic of a field that's growing really quickly, but it's also an artifact of strange incentive structures in academic machine learning, and the fact that sometimes machine learning is just really hard. At the same time, a high quality of academic work is critical for maintaining the reputation of the field, so in this episode we walk through a recent paper that spells out some of the most common shortcomings of academic machine learning papers and what we can do to make things better. Relevant links: https://arxiv.org/abs/1807.03341
      15
      15
        0:00:00 / 0:00:00