Alexander Denecke, Heiko Wersing, Jochen Steil, Edgar Körner,
"Incremental Figure-Ground Segmentation using localized adaptive metrics in LVQ",
Proc. 7th International Workshop on Self-Organizing Maps (WSOM), 2009.
Abstract
Abstract. Vector quantization methods are confronted with a model selection problem, namely the number of prototypical feature representatives to model each class. In this paper we present an incremental learning scheme in the context of figure-ground segmentation. In presence of local adaptive metrics and supervised noisy information we use a parallel evaluation scheme combined with a local utility function to organize a learning vector quantiza...
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Martin Heckmann, Holger Brandl, Xavier Domont, Miguel Vaz, Jens Schmüdderich, Bram Bolder, Frank Joublin, Christian Goerick,
"Language Acquisition Embedded into Tutor-Robot Interaction",
Proc. ACORNS Workshop Computational Models of Language Evolution, Acquisition and Processing, 2009.
Abstract
Children acquire language to a large extend in the interaction with their caregivers. Inspired by this observation we develop computational models and artifacts for the acquisition of language in an interactive scenario. The artifact bootstraps its representations with little a priori knowledge and can be taught by a human tutor. In this framework we investigate different aspects of the speech acquisition process. This encompasses the learning of...
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Martin Heckmann, Xavier Domont, Frank Joublin, Christian Goerick,
"Combining Auditory Inspirations and Hierarchical Feature Extraction for Robust Speech Recognition",
Proceedings of NAG-DAGA: International Conference on Acoustics, 2009.
Abstract
We present speech features inspired by the processing in the auditory periphery and the receptive fields found in the auditory cortex. They have a hierarchical organization and jointly evaluate variations in the spectro-temporal domain. This is why we termed them Hierarchical Spectro-Temporal (HIST) features. For their calculation we apply a Gammatone filterbank to transform the signal into the spectral domain. In a preprocessing based on local c...
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Friedemann Pulvermüller and Andreas Knoblauch,
"Discrete combinatorial circuits emerging in neural networks: A mechanism for rules of grammar in the human brain?",
Neural Networks, vol. 22, pp. 161–172, 2009.
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Tobias Rodemann, Frank Joublin, Christian Goerick,
"Audio Proto Objects for Improved Sound Localization",
IEEE-RSJ International Conference on Intelligent Robot and Systems (IROS 2009), 2009.
Abstract
In this article we present a new framework for auditory processing that combines feature extraction and grouping processes to form what we call audio proto objects. These proto objects combine an arbitrary number of audio features in a compact representation that allows a more precise sound localization and also better interfacing to behavior-control in robotics. We compare our standard sound localization system with the new approach in several s...
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Matthew Howard,
"Learning Control Policies from Constrained Motion",
University of Edinburgh, 2009.
Abstract
Many everyday human skills can be framed in terms of performing some task subject to constraints imposed by the task or the environment. Constraints are usually unobservable and frequently change between contexts. In this thesis, we explore the problem of learning control policies from data containing variable, dynamic and non-linear constraints on motion. We show that an effective approach for doing this is to learn the unconstrained policy in a...
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Thomas Weisswange, Constantin Rothkopf, Tobias Rodemann, Jochen Triesch,
"Can reinforcement learning explain the development of causal inference in multisensory integration?",
Proceedings of the IEEE 8th International Conference on Development and Learning (ICDL), 2009.
Abstract
Bayesian inference techniques have been used to understand the performance of human subjects on a large number of sensory tasks. Particularly, it has been shown that humans integrate sensory inputs from multiple cues in an optimal way in many conditions. Recently it has also been proposed that causal inference [1] can well describe the way humans select the most plausible model for a given input. It is still unclear how those problems are solved ...
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Matthias Rolf, Jochen Steil, Michael Gienger,
"Efficient exploration and learning of whole body kinematics",
Proceedings of the The 8th International Conference on Development and Learning, 2009.
Abstract
We present a neural network approach to early motor learning. The goal is to explore the needs for bootstrapping the control of hand movements in a biologically plausible learning scenario. The model is applied to the control of hand postures of the humanoid robot ASIMO by means of full upper body movements. For training, we use an efficient online scheme for recurrent reservoir networks consisting of supervised backpropagation-decorrelation outp...
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Daniel Weiler and Julian Eggert,
"Level-Set Segmentation with Contour based Object Representation",
Proceedings of the 2009 International Joint Conference on Neural Networks (IJCNN), 2009.
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Yaochu Jin, Honglian Guo, Yan Meng,
"Robustness analysis and failure recovery of a bio-inspired self-organizing multi-robot system",
Third IEEE International Conference on Self-Adaptive and Self-organizing Systems, pp. 154–164, 2009.
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