13 must-read papers from AI experts - KDnuggets

All of the below papers are free to access and cover a range of topics from Hypergradients to modeling yield response for CNNs. Each expert also included a reason as to why the paper was picked as well as a short bio. We spoke to Jeff back in January, and at that time, he couldn't pick just one paper as a must-read, so we let him pick two. This paper unpacks two key talking points, the limitations of sparse training data, and also if recurrent networks can support meta-learning in a fully supervised context. These points are addressed in seven proof-of-concept experiments, each of which examines a key aspect of deep meta-RL.