Let (X, Y) XY = N(0, XY) bejointly variables. Thisrateisinline(3), which boundmeaningfulk-SMIisitself k-SMIdecompositionGiven in Gaussian 36,37], we k-SMIintoa(X, Y) µXY 2 P(Rdx Rdy), let(X ,Y ) XY :=N(0, XY)bejointly (X, Y).

Given the complexities inherent in visual scenes, such as object occlusion, a comprehensive understanding often requires observation from multiple viewpoints. Existing multi-viewpoint object-centric learning methods typically employ random or sequential viewpoint selection strategies.

The variance was computed empirically overk = 100 normally distributed samplesw. As can be seen, the variance of the kernel tends to zero only when both widths increase. The hyperkernel used corresponds to the infinite width limit ofthesame architecture. As can be seen in Figure 1, whenf is wide and kept fixed, there is a clear improvement in test performance as the width ofg increases, for every learning rate in which the networks provide non-trivial performance. Whenf is wide and kept fixed, a deeperg incurs slower training and lower overall test performance.

Moreover, our methods aretheoretically guaranteed toconvergetothePareto front.