However, achieving this capability necessitates not only an effective scene representation but also an understanding of the mechanisms governing interactions among object subsets. Recent studies have made significant progress in representing scenes using object slots.

However, achieving this capability necessitates not only an effective scene representation but also an understanding of the mechanisms governing interactions among object subsets. Recent studies have made significant progress in representing scenes using object slots.

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

We propose a general class of sample based explanations of machine learning models, which we term generalized representers . To measure the effect of a training sample on a model's test prediction, generalized representers use two components: a global sample importance that quantifies the importance of the training point to the model and is invariant to test samples, and a local sample importance that measures similarity between the training sample and the test point with a kernel.

But theguarantees have strong conditions that donot often hold inpractice, resulting indegenerate component optimization problems; and weshowthat the ad-hoc regularization used to prevent degeneracyin practice can cause BVI to fail in unintuitiveways.