202006101316

Problems with Machine Learning Scholarship

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via Science.

  1. Pruning: [[network-pruning]]
  2. Recommendation Systems: seems like it’s not all good news on this front either: (Dacrema, Cremonesi, and Jannach 2019Dacrema, Maurizio Ferrari, Paolo Cremonesi, and Dietmar Jannach. 2019. “Are we really making much progress? A worrying analysis of recent neural recommendation approaches.” In RecSys 2019 - 13th Acm Conference on Recommender Systems, 101–9. New York, NY, USA: Politecnico di Milano, Milan, Italy; ACM.)
  3. Metric Learning (arXiv): looking at different loss functions doesn’t really help that much.
  4. Adversarial Training: (Rice, Wong, and Kolter 2020Rice, Leslie, Eric Wong, and J Zico Kolter. 2020. “Overfitting in adversarially robust deep learning.” arXiv.org, February. http://arxiv.org/abs/2002.11569v2.)