Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by GNNs remains a challenging open problem. The leading method independently addresses the local explanations (i.e., important subgraph structure and node features) to interpret why a GNN model makes the prediction for a single instance, e.g. a node or a graph… As a result, the explanation generated is painstakingly customized for each instance. The unique explanation interpreting each instance independently is not sufficient to provide a global understanding of […]

We present three problems modeled after animal learning experiments designed to test online state construction or representation learning algorithms. Our test problems require the learning system to construct compact summaries of their past interaction with the world in order to predict the future, updating online and incrementally on each time step without an explicit training-testing split… The majority of recent work in Deep Reinforcement Learning focuses on either fully observable tasks, or games where stacking a handful of recent frames […]