The caffe framework is one of the leading deep learning toolboxes in the machine learning and computer vision community. While it offers efficiency and configurability, it falls short of a full interface to Python. With increasingly involved …
We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about …
Since the introduction of Random Forests in the 80's they have been a frequently used statistical tool for a variety of machine learning tasks. Many different training algorithms and model adaptions demonstrate the versatility of the forests. This …
Many large-scale systems benefit from an organizational structure to provide for problem decomposition. A pivotal problem solving setting is given by hierarchical control systems familiar from hierarchical task networks. If these structures can be …
Organizational structures such as hierarchies provide an effective means to deal with the increasing complexity found in large-scale energy systems. In hierarchical systems, the concrete functions describing the subsystems can be replaced by abstract …
The entropy measurement function is a central element of decision forest induction. The Shannon entropy and other generalized entropies such as the Renyi and Tsallis entropy are designed to fulfill the Khinchin-Shannon axioms. Whereas these axioms …
Feature learning has the aim to take away the hassle of hand-designing features for machine learning tasks. Since the feature design process is tedious and requires a lot of experience, an automated solution is of great interest. However, an …