Neural Information Processing Systems http://nips.cc/

Sequence-to-sequence learning with neural networks has become the de facto standard forsequence prediction tasks.

Imitation learning has proven to be a powerful tool for training complex visuo-motor policies. However, current methods often require hundreds to thousands of expert demonstrations to handle high-dimensional visual observations.

When one agent interacts with a multi-agent environment, it is challenging to deal with various opponents unseen before.

Even superforecasters are guessing that they'll soon be obsolete. To live in time is to wonder what will happen next. In every human society, there are people who obsess over the world's patterns to predict the future. In antiquity, they told kings which stars would appear at nightfall. Today they build the quantitative models that nudge governments into opening spigots of capital.

Motivatedbyrecent advances ingeometric representation learning, we propose a novel GNN architecture for learning representations on Riemannian manifolds with differentiable exponential and logarithmic maps.