In this paper I try to address the following question: “Is it really worth to build a k-Nearest Neighbor graph from tabular data and then apply deep graph nets on top of it to classify each sample in the table?

In this paper we combine uncertainty-biased molecular dynamics with active learning to show how we can learn machine learning interatomic potential (MLIP) models that are more robust to predictions on extrapolative regions.

We propose a temporal knowledge graph baseline based on the simple principle of recurrency. Surprisingly, a simple approach like this beats state of the art methods based on representation learning on 3 out of 5 commonly used datasets!

We propose a probabilistic model that bridges graph machine learning and sum-product networks to tractably answer probabilistic queries. Our Graph-induced Sum-Product Network can solve unsupervised and supervised graph tasks and it is especially effective in modeling the missing data distribution as well as exploiting unlabeled data in a scarce supervision scenario.

In this paper I try to address the following question: “Is it really worth to build a k-Nearest Neighbor graph from tabular data and then apply deep graph nets on top of it to classify each sample in the table?