To this end, we propose an analogous score function called the "Concretescore",ageneralizationofthe(Stein)scorefordiscretesettings.

We analyze the information-theoretic limits for the recovery of node labels in several network models.

First, we propose a theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound.

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

Top: inputimageswiththeaxisof symmetry superimposed (showningreen). Infact,ourmethodbuildson[38]and also learns a dense geometric embedding for objects, however, by using a different supervision principle,symmetry.

Kernel dimensionality reduction (KDR) algorithms find a low dimensional representation of the original data by optimizing kernel dependency measures that are capable ofcapturing nonlinear relationships.