The app reads your email inbox and your meeting calendar, then gives you a short audio summary. It can help you spend less time scrolling, but of course, there are privacy drawbacks to consider.

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.