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The current flood of information in all areas of machine learning research,from computer vision to reinforcement learning, has made it difficult to makeaggregate scientific inferences. It can be challenging to distill a myriad ofsimilar papers into a set of useful principles, to determine which newmethodologies to use for a particular application, and to be confident that onehas compared against all relevant related work when developing new ideas.However, such a rapidly growing body of research literature is a problem thatother fields have already faced - in particular, medicine and epidemiology. Inthose fields, systematic reviews and meta-analyses have been used exactly fordealing with these issues and it is not uncommon for entire journals to bededicated to such analyses. Here, we suggest the field of machine learningmight similarly benefit from meta-analysis and systematic review, and weencourage further discussion and development along this direction.

Distilling Information from a Flood: A Possibility for the Use of Meta-Analysis and Systematic Review in Machine Learning Research