Esse é um artigo bem antigo que escrevi em 2013, mas face aos recentes eventos na minha carreira acadêmica estou postando publicamente para ajudar quem se propõe a fazer tal tarefa.
This piece of Rajiv Shah called “Stand up for Best Practices” that involves a well known scientific journal Nature shows the academic rigor failed during several layers down and why reproducibility matters.
From MIT Tech Review article called “Google shows how AI might detect lung cancer faster and more reliably” we have the following information: Early warning: Danial Tse, a researcher at Google, developed an algorithm that beat a number of trained radiologists in testing.
Most of the time we completely rely in the default parameters of Machine Learning Algorithm and this fact can hide that sometimes we can make wrong statements about the ‘efficiency’ of some algorithm.
Start note: Favio Vazquez made a great job in his article about it with a lot of charts and showing that in modern Machine Learning approach with the tools that we currently have the problems of replication and methodology are being tackled.
A post about ROC analysis becomes a small lecture about decision analysis: It’s good for researchers to present their raw data, along with clean summary analyses.
Abstract: We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.