Afterwards, based onthe enriched representation, we propose one novel graphpointnet module, relyingonthegraphattention blocktodynamically compose and update each point representation within the local point cloud structure.

To alleviate the risk of model-misspecification in MLE, we propose to generate samples from the generativemodel and monitor the quality of the samples in the process of training until the samples and the real data are indistinguishable.

Figure 1: Prediction risk of ridge regression on trained CK features (erf) after one feature learning step.

Empirical data can often be considered as samples from a set of probability distributions.

Wedemonstrate theproposed methodology onahypertension dataset,showingthatourprescribed treatment leadstoalarger reduction in the systolic blood pressure compared to a series of alternatives.

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