By Stefan Wermter, Cornelius Weber, Włodzisław Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Alessandro E. P. Villa (eds.)

The booklet constitutes the lawsuits of the twenty fourth overseas convention on man made Neural Networks, ICANN 2014, held in Hamburg, Germany, in September 2014.
The 107 papers integrated within the complaints have been rigorously reviewed and chosen from 173 submissions. the focal point of the papers is on following themes: recurrent networks; aggressive studying and self-organisation; clustering and category; timber and graphs; human-machine interplay; deep networks; thought; reinforcement studying and motion; imaginative and prescient; supervised studying; dynamical types and time sequence; neuroscience; and applications.

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Extra resources for Artificial Neural Networks and Machine Learning – ICANN 2014: 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings

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Figure 2(a) depicts the performance of the FTRNN with and without regularization. The performance metric is the median of five runs of the average MSE per state component per predicted time step. At the beginning of each run, a new parameter initialization was sampled at random. The experiments on the cart-pole simulation revealed superior performance of the regularized FTRNN over the plain FTRNN for |DT,2 | = 625. The smallest error was obtained by setting λ = 10−2 . A large coefficient reduced the error for |DT,2 | = 625 but increased it for |DT,2 | ∈ {1250, 2500, 5000, 10 000} compared to the plain FTRNN.

1(b), it is clear that the CTRNN failed to generate some patterns whose noise variance was small. For example, we can observe the three patterns with the smallest noise variance (1, 5, 9) are corrupted by the three patterns with the largest noise variance (4, 12, 8). The network also failed to reproduce the stochastic properties of the training patterns. In contrast, the S-CTRNN correctly reproduces the fluctuating Lissajous curves (Fig. 1(c)). Figure 2 shows the results for other CTRNNs in which different randomly chosen values were used for parameter initialization before network training.

Although all parameters (including the weights and biases, which are common for all sequences, and the initial states, which are different for each sequence) are optimized during the training process, only the initial states are optimized during the recognition process. 1 Numerical Experiment Training Sequences The task for the CTRNN and the S-CTRNN was learning to predict the temporal developments of 12 fluctuating two-dimensional Lissajous curves with a sequence length T = 1000. These fluctuating curves were obtained by adding (s) (s) σi }2 to each clean Lissajous curve, Gaussian noise ({ˆ σi }2 ) with variance {ˆ where i ∈ {1, 2} represents the axis of two-dimensional training data space.

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