Anton Korell,
"Recherchemethoden und Portfoliobewertung im Bereich neuer Technologien am Beispiel der Computational Intelligence",
Hochschule Amberg-Weiden, 2010.
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Marc Henniges, Gervasio Puertas, Jörg Bornschein, Julian Eggert, Jörg Lücke,
"Binary Sparse Coding",
Proceedings of the LVA, issue 1st edition, 2010.
Abstract
We study a sparse coding learning algorithm that allows for a simultaneous learning of the data sparseness and the basis functions. The algorithm is derived based on a generative model with binary latent variables instead of continuous-valued latents as used in classical sparse coding. We apply a novel approach to perform maximum likelihood parameter estimation that allows for an efficient estimation of all model parameters. The approach is a new...
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Manuel Mühlig, Michael Gienger, Jochen Steil,
"Human-Robot Interaction for Learning and Adaptation of Object Movements",
Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS), 2010.
Abstract
In this paper we present a new robot control and learning framework. By integrating previously presented as well as new methods, the robot is able to learn an invariant and generic movement representation from a human tutor. We argue that in order to apply such generic representations to new situations and thus create a flexible system, the use of interaction is beneficial. The interaction is based on a kinematically controlled model of a human t...
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Ming Liu,
"Auditory Gist",
FIAS, Frankfurt University, 2010.
Abstract
In this thesis, Auditory gist analysis and sound signal processing methods for auditory gist classification are developed. Generally, the thesis concerns the problem of getting auditory gist. The auditory gist, general assumed to be a perception of a sound. The auditory gist idea is from visual gist. Just see an image, people can immediately get a rough idea about the image¡Çs main scene information, this is visual gist. By the same way, the gi...
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Matthias Rolf, Jochen Steil, Michael Gienger,
"Learning Flexible Full Body Kinematics for Humanoid Tool Use",
Proceedings of the International Symposium on Learning and Adaptive Behavior in Robotic Systems, 2010.
Abstract
We show that inverse kinematics of different tools can be efficiently learned with a single recurrent neural network. Our model exploits all upper body degrees of freedom of the Honda’s humanoid robot research platform. Both hands are controlled at the same time with parametrized tool geometry. We show that generalization both in space as well as across tools is possible from very few training data. The network even permits extrapolation beyond...
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Coralie Camus,
"Extraction of optical flow fields from from a "silicon retina" optical sensor",
TU Darmstadt, 2010.
Abstract
Traditional vision sensors (cameras) present some limits like a high latency that the dynamic vision sensor from ETH Zurich try to minimize. This project aims to extract motion vectors from events provided by the DVS128 in an asynchronous stream of data. In contrast to traditional computation using a xed time distance and estimating the spatial distances, the spatial distance are here xed and the time intervals estimated. For this, several Reicha...
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Lars Gräning, Nikola Aulig, Markus Olhofer,
"Towards Directed Open-Ended Search by a Novelty Guided Evolution Strategy",
Parallel Problem Solving from Nature - PPSN XI, pp. 71–80, 2010.
Abstract
In the conceptional phases of design optimization tasks it is required to find new innovative solutions to a given problem. Although evolutionary algorithms are suitable methods to this problem, the search of a wide range of the solution space in order to identify novel concepts is mainly driven by random processes and is therefore a demanding task, especially for high dimensional problems. To improve the exploration of the design space additiona...
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Stefan Menzel, Markus Olhofer, Bernhard Sendhoff,
"A Metamodel-driven Interactive Framework for a Designer Assistance System",
Proceedings of the 11th International Design Conference – Design 2010, pp. 1371–1380, 2010.
Abstract
The design of innovative products in the automotive industry is influenced by multiple criteria dominated by both human creativity and technical requirements. Thus the generation of a prototype is an adaptive process which iteratively integrates the needs of various disciplines, working on different timescales. This paper proposes and evaluates a styling design framework which introduces the application of neural networks for fast estimation of t...
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Martin Heckmann,
"Supervised vs. Unsupervised Learning of Spectro Temporal Speech Features",
Statistical And Perceptual Audition (SAPA), 2010.
Abstract
To overcome limitations of purely spectral speech features we previously introduced Hierarchical Spectro-Temporal (HIST) features. We could show that a combination of HIST and standard features does reduce recognition errors in clean and in noise. The HIST features consist of two hierarchical layers where the corresponding filter functions are learned in a data driven way. In this paper we investigate how different learning methodologies applied ...
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Robert Kastner, Thomas Michalke, Thomas Burbach, Jannik Fritsch, Christian Goerick,
"Attention-Based Traffic Sign Recognition with an Array of Weak Classifiers",
IEEE Intelligent Vehicles Symposium (IV), 2010.
Abstract
Currently available traffic sign recognition systems typically focus on a single class of traffic sign and therefore, the algorithms are optimized to find only this specific class. To this end, a number of approaches for real time capable classification of mostly circular signs exist. Nevertheless, to simultaneously recognize a number of classes a different way has to be taken. This paper presents a real-time capable approach, which uses a two-ti...
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