Reading suggestions to keep you up-to-date with the latest and classic breakthroughs in AI and Data Science. Beyond just healthcare, where the writers main applications are.
AI Papers to read in 2020 via KDNuggets By Ygor Rebouças Serpa, developing explainable AI tools for the healthcare industry
Artificial Intelligence is one of the most rapidly growing fields in science and is one of the most sought skills of the past few years, commonly labeled as Data Science. The area has far-reaching applications, being usually divided by input type: text, audio, image, video, or graph; or by problem formulation: supervised, unsupervised, and reinforcement learning. Keeping up with everything is a massive endeavor and usually ends up being a frustrating attempt. In this spirit, I present some reading suggestions to keep you updated on the latest and classic breakthroughs in AI and Data Science.
Although most papers I listed deal with image and text, many of their concepts are fairly input agnostic and provide insight far beyond vision and language tasks. Alongside each suggestion, I listed some of the reasons I believe you should read (or re-read) the paper and added some further readings, in case you want to dive a bit deeper into a given subject.
Before we begin, I would like to apologize to the Audio and Reinforcement Learning communities for not adding these subjects to the list, as I have only limited experience with both.
Here we go. ... ' (Detail at the link) ....
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