In the most basic setting of this problem, an agent has to pull one among a finite set of arms (or actions), and she receives a reward that depends on the chosen action.

The statistics of the five datasets can be seen in the Table 1.

We present a novel framework for unsupervised object-centric 3D scene understanding that generalizes robustly to out-of-distribution images.

In many sequential tasks, a model needs to remember relevant events from the distant past to make correct predictions. Unfortunately, a straightforward application ofgradient based training requires intermediate computations tobestored for every element of a sequence. This requires to store prohibitively large intermediate data ifasequence consists ofthousands oreven millions elements, and asaresult, makeslearning ofverylong-term dependencies infeasible.