ICANN
2010
Springer
13 years 11 months ago
2010
Springer
Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures however ...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
3D shape determines an object's physical properties to a large degree. In this article, we introduce an autonomous learning system for categorizing 3D shape of simulated objec...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. Local air quality forecasting can be made on the basis of meteorological and air pollution time series. Such data contain redundant information. Partial mutual informatio...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. In this article we exploit the discrete-time dynamics of a single neuron with self-connection to systematically design simple signal filters. Due to hysteresis effects an...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
Learning processes allow the central nervous system to learn relationships between stimuli. Even stimuli from different modalities can easily be associated, and these associations ...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
Abstract. Spiking Neuron Networks (SNNs) overcome the computational power of neural networks made of thresholds or sigmoidal units. Indeed, SNNs add a new dimension, the temporal a...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
Many statistical methods have been proposed to estimate causal models in classical situations with fewer variables than observations. However, modern datasets including gene expres...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
With the term super-resolution we refer to the problem of reconstructing an image of higher resolution than that of unregistered and degraded observations. Typically, the reconstru...
ICANN
2010
Springer
13 years 11 months ago
2010
Springer
In this work, a novel probability distribution is proposed to model sparse directional data. The Directional Laplacian Distribution (DLD) is a hybrid between the linear Laplacian d...