Learning from partially supervised data using mixture models and belief functions.

In practice, popular SSL methods as Standard Laplacian (SL), Normalized Laplacian (NL) or Page Rank (PR), exploit those operators defined on graphs to spread the labels and, from

The framework we present in this paper extends these approaches by learning si- multaneously a single shared dictionary as well as multiple decision functions for different

q Unconstrained Convex Optimization q Consistency of the ERM principle q Timeline of Deep Learning q Multi-class classification.. q Unsupervised and

We propose an online SSL algorithm that uses a combi- nation of quantization and regularization to obtain fast and accurate performance with provable performance bounds. Our

A typical scenario for apprenticeship learning is to have an expert performing a few optimal (or close to the optimal) trajectories and use these to learn a good policy.. In this

▶ Our encoder transforms the data into feature representations, from these representations we infer a Gaussian latent variable to provide more robust set of features and a

In the following set of experiments with the practical FOM and CG algorithms, we assume that the products Ap can be computed in double, single or half precision (with

In this study using animal models (i.e., accelerated-catheter thrombosis, arteriove- nous shunt, and CPB with cardiac surgery), we assessed the clinical applicability of Ir-CPI as