Aisasetofweightedlinear combinations of the entries ofX, this problem is often referred to as thematrix sensingproblem.

This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction.

Hence, a "fair" reward allocation scheme is desirable to give all parties enough incentives to join the collaboration.

The proposed layers lead toamore data efficient representation and areduction in computation by exploiting symmetry. We evaluate chirality nets on the task ofhuman poseregression, which naturally exploits theleft/right mirroring ofthe human body.

Weevaluate ourmodel onbothsynthetic andin-the-wild Internet imagery and demonstrate its advantages in terms of generalization and accuracy.

SynCx outperforms or is strongly competitive with current models for unsupervised object discovery.

A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representationsofdata.

Specifically,GSPENs combine thecapabilities ofclassicalstructured prediction models andSPENs andhavetheability toexplicitly model localstructure whenknown or assumed, while providing the ability to learn an unknown or more global structure implicitly.

Thus, one important question one can ask here is how welldFnn(ˆµn,ˆνm)approximatesdFnn(µ,ν).

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