Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2017; 26th International C Alessandra Lintas,Stefano Rovetta,Alessandro E.P. Confe

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Artificial Neural Networks and Machine Learning – ICANN 2017978-3-319-68600-4Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Semi-supervised Phoneme Recognition with Recurrent Ladder Networkse being compatible with many existing neural architectures. We present the recurrent ladder network, a novel modification of the ladder network, for semi-supervised learning of recurrent neural networks which we evaluate with a phoneme recognition task on the TIMIT corpus. Our results show that the

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Mixing Actual and Predicted Sensory States Based on Uncertainty Estimation for Flexible and Robust Rbot behavior. We employ the so-called stochastic continuous-time RNN (S-CTRNN), which can learn to predict the mean and variance (or uncertainty) of subsequent sensorimotor information. Our proposed method uses this estimated uncertainty to determine a mixture ratio for combining actual and predicte

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Neural End-to-End Self-learning of Visuomotor Skills by Environment Interactionex environments, generating suitable training data is time-consuming and depends on the availability of accurate robot models. Deep reinforcement learning alleviates this challenge by letting robots learn in an unsupervised manner through trial and error at the cost of long training times. In contra