Finally, we report empirical evaluation on synthetic and real-world data showing that our proposed SVD-based method does indeed achieve a better cost than other widely-used heurstics and also results in a better classification accuracy when the underlying problem was that of multi-class classification.

Computer vision tasks often have side information available that is helpful to solve the task. For example, for crowd counting, the camera perspective (e.g., camera angle and height) gives a clue about the appearance and scale of people in the scene. While side information has been shown to be useful for counting systems using traditional hand-crafted features, it has not been fully utilized in counting systems based on deep learning.

Multiple-domain learning contains in turn two sub-challenges.

It is a grid free inference approach, which, for fully observable systems is at times competitive with numerical integration.

In this work we present such an estimator, called CRR .

However, the Achilles' heel of all these approaches is that

For example, replicating the straggling task on another available node is a common approach to deal with stragglers (e.g., [

Some of the most important contributions in this domain are due to Nemirovski et al. [

Collecting large training datasets, annotated with high-quality labels, is costly and time-consuming. This paper proposes a novel framework for training deep convolutional neural networks from noisy labeled datasets that can be obtained cheaply. The problem is formulated using an undirected graphical model that represents the relationship between noisy and clean labels, trained in a semi-supervised setting.