Frequentist parameter estimation with supervised learning

Frequentist parameter estimation

Hyper-parameter Learning for Graph Based Semi-supervised Learning Algorithms

Constrained Parameter Estimation for Semi-supervised Learning: The Case of the Nearest Mean Classifier

A rather simple semi-supervised version of the equally simple nearest mean classifier is presented. However simple, the proposed approach is of practical interest as the nearest mean classifier remains a relevant tool in biomedical applications or other areas dealing with relatively high-dimensional feature spaces or small sample sizes. More importantly, the performance of our semi-supervised n...

Hand pose estimation through semi-supervised and weakly-supervised learning

We propose a method for hand pose estimation based on a deep regressor trained on two different kinds of input. Raw depth data is fused with an intermediate representation in the form of a segmentation of the hand into parts. This intermediate representation contains important topological information and provides useful cues for reasoning about joint locations. The mapping from raw depth to seg...

Weakly-Supervised Learning with Cost-Augmented Contrastive Estimation

We generalize contrastive estimation in two ways that permit adding more knowledge to unsupervised learning. The first allows the modeler to specify not only the set of corrupted inputs for each observation, but also how bad each one is. The second allows specifying structural preferences on the latent variable used to explain the observations. They require setting additional hyperparameters, w...