386 research outputs found
Inside the German Miracle. How trade unions shape the future of industrial working conditions
Low unemployment rates during the crisis and a quick recovery are the main characteristics of the «German miracle». The general role of social partners and in particular the role of trade unions in the affected sectors appear to be undervalued especially in macro-level economic analysis. This article provides an insight into the activities of the metalworkers union (IG Metall) in developing strategies for innovative and sustainable solutions to improve future production and working conditions
Integration of Leaky-Integrate-and-Fire-Neurons in Deep Learning Architectures
Up to now, modern Machine Learning is mainly based on fitting high
dimensional functions to enormous data sets, taking advantage of huge hardware
resources. We show that biologically inspired neuron models such as the
Leaky-Integrate-and-Fire (LIF) neurons provide novel and efficient ways of
information encoding. They can be integrated in Machine Learning models, and
are a potential target to improve Machine Learning performance.
Thus, we derived simple update-rules for the LIF units from the differential
equations, which are easy to numerically integrate. We apply a novel approach
to train the LIF units supervisedly via backpropagation, by assigning a
constant value to the derivative of the neuron activation function exclusively
for the backpropagation step. This simple mathematical trick helps to
distribute the error between the neurons of the pre-connected layer. We apply
our method to the IRIS blossoms image data set and show that the training
technique can be used to train LIF neurons on image classification tasks.
Furthermore, we show how to integrate our method in the KERAS (tensorflow)
framework and efficiently run it on GPUs. To generate a deeper understanding of
the mechanisms during training we developed interactive illustrations, which we
provide online.
With this study we want to contribute to the current efforts to enhance
Machine Intelligence by integrating principles from biology
How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks
Deep neural networks typically outperform more traditional machine learning
models in their ability to classify complex data, and yet is not clear how the
individual hidden layers of a deep network contribute to the overall
classification performance. We thus introduce a Generalized Discrimination
Value (GDV) that measures, in a non-invasive manner, how well different data
classes separate in each given network layer. The GDV can be used for the
automatic tuning of hyper-parameters, such as the width profile and the total
depth of a network. Moreover, the layer-dependent GDV(L) provides new insights
into the data transformations that self-organize during training: In the case
of multi-layer perceptrons trained with error backpropagation, we find that
classification of highly complex data sets requires a temporal {\em reduction}
of class separability, marked by a characteristic 'energy barrier' in the
initial part of the GDV(L) curve. Even more surprisingly, for a given data set,
the GDV(L) is running through a fixed 'master curve', independently from the
total number of network layers. Furthermore, applying the GDV to Deep Belief
Networks reveals that also unsupervised training with the Contrastive
Divergence method can systematically increase class separability over tens of
layers, even though the system does not 'know' the desired class labels. These
results indicate that the GDV may become a useful tool to open the black box of
deep learning
The stochastic resonance model of auditory perception:A unified explanation of tinnitus development, Zwicker tone illusion, and residual inhibition
Stochastic resonance (SR) has been proposed to play a major role in auditory perception, and to maintain optimal information transmission from the cochlea to the auditory system. By this, the auditory system could adapt to changes of the auditory input at second or even sub-second timescales. In case of reduced auditory input, somatosensory projections to the dorsal cochlear nucleus would be disinhibited in order to improve hearing thresholds by means of SR. As a side effect, the increased somatosensory input corresponding to the observed tinnitus-associated neuronal hyperactivity is then perceived as tinnitus. In addition, the model can also explain transient phantom tone perceptions occurring after ear plugging, or the Zwicker tone illusion. Vice versa, the model predicts that via stimulation with acoustic noise, SR would not be needed to optimize information transmission, and hence somatosensory noise would be tuned down, resulting in a transient vanishing of tinnitus, an effect referred to as residual inhibition.</p
Sparsity through evolutionary pruning prevents neuronal networks from overfitting
Modern Machine learning techniques take advantage of the exponentially rising
calculation power in new generation processor units. Thus, the number of
parameters which are trained to resolve complex tasks was highly increased over
the last decades. However, still the networks fail - in contrast to our brain -
to develop general intelligence in the sense of being able to solve several
complex tasks with only one network architecture. This could be the case
because the brain is not a randomly initialized neural network, which has to be
trained by simply investing a lot of calculation power, but has from birth some
fixed hierarchical structure. To make progress in decoding the structural basis
of biological neural networks we here chose a bottom-up approach, where we
evolutionarily trained small neural networks in performing a maze task. This
simple maze task requires dynamical decision making with delayed rewards. We
were able to show that during the evolutionary optimization random severance of
connections lead to better generalization performance of the networks compared
to fully connected networks. We conclude that sparsity is a central property of
neural networks and should be considered for modern Machine learning
approaches
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