Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

In this work we address part-based object detection under variability of part shapes and spatial relations. Our approach bases on the hierarchical finite element modeling concept of Engel and Tönnies [ET09a, ET09b]. They model object parts by elastic materials, which adapt to image structures via image-derived forces. Spatial part relations are realized through additional layers of elastic mate...

-Recurrent neural networks (RNNs) with linearized dynamics have shown great promise in solving continuous valued optimization problems subject to bound constraints. Building on this progress, a novel method of constrained hierarchical multi-scale optimization is developed that applies to a wide range of optimization problems and signal decomposition tasks. Central to the underlying concept is t...