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

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.

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).

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

Directly solving the two layers is still an ill-posed problem, but we find that the problem has a closedform solution when the semireflector surface normal is known.

Facial action units (AUs) recognition is essential for emotion analysis and has been widely applied in mental state analysis. Existing work on AU recognition usually requires big face dataset with accurate AU labels. However, manual AU annotation requires expertise and can be time-consuming. In this work, we propose asemi-supervised approach forAUrecognition utilizing alargenumber of web face images without AU labels and a small face dataset with AU labels inspired by the co-training methods.

In addition to its use in regression, the relationship between the sinc kernel and the classic theory is illuminated, in particular, the Shannon-Nyquist theorem is interpreted as posterior reconstruction under the proposed kernel.

We study the problem of fitting an ultrametric distance to a dissimilarity graph in the context of hierarchical cluster analysis.

What policy should be employed in a Markov decision process with uncertain parameters?