Here,weproposea spatio-translational embeddingmodel,called BoxE,that simultaneously addresses all these limitations.

Amongst those functions, the simplest are single-index modelsf(x) = ϕ(x θ), where the labels are generated by an arbitrary non-linear scalar link functionϕ applied to an unknown one-dimensional projectionθ of the input data.

Our analysis reveals that the Polyak step-size adapts toany directional smoothness to obtain the tightest possible convergence rate.

Wefirst learn tosolvethree base tasks: collecting purple objects (Figure 5)collecting blue objects (Figure 6)andcollecting squares (Figure 7).

A major challenge is thus in designing sample-efficient agents that can transfer their existing knowledge to solve new tasks quickly.

More specifically,for the output features ofthe penultimate layer, for each class the within-class features converge to their means, and the means of different classes exhibit a certain tight frame structure, which is also aligned withthelastlayer'sclassifier.

Deep learning algorithms have shown significant development thanks tothelargepre-collected dataset, such as SQuAD [47]innatural language processing (NLP), and ImageNet [4] in computer vision.