Much of the promise of these models relies on learned concepts being high quality .

Thisresult shows that visual grounding/feature attribution can be ineffective at resolving shortcuts.

Inspired by the depthwise separable convolution used in MobileNet [1], we extend this separable idea to graph convolution. We only perform MLPs on point features directly to learn thechannelcorrelation, andleverages theanisotropic reduction toaggregatethespatial correlation.

A reinforcement learning (RL) agent is tasked with two fundamental, interdependent problems: exploration(howtodiscoverusefuldata),andcreditassignment(howtoincorporateit). The simplest way of estimating the value function is by averaging returns (futurediscountedsumsofrewards)startingfromtaking ainx.