How You Should Read a Machine Learning Paper

Although this point is sometimes obvious, it is necessary to highlight it. We tend to think that papers, being scientific documents, are produced in a perfectly rigorous way, they follow agreed conventions and methodologies. Nothing could be further from the truth. Being Machine Learning one of the most multidisciplinary scientific fields, as it feeds from Mathematics, Linguistics, Computer Science, Signal Processing… Each one of them has its unique set of methodologies. This means that in one paper a neural network is explained from its layer structure, in another paper through a signal processing algorithm, and in another through Bayesian probability formulas. To fully comprehend a topic, normally it is necessary to analyze it from all its perspectives and if you want to learn more about a specific way of conceptualizing the problem (ie, Bayesian probability) you should review publications with shared magazines or shared conferences which would usually have a similar perspective. Seek opinions on the paper and learn to be critical. When you read a paper, you should bear in mind that although it is a document that has passed some verification tests, this does not make it an error-proof document (especially when reading preprints).