Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2018; 27th International C Věra Kůrková,Yannis Manolopoulos,Ilias Maglogianni Confe

DRAFT

Aufzeichnung von Fernsehprogrammen,mage recognition field due to the discriminative power of Convolutional Neural Network (CNN). In this paper, we first propose a novel Multi-Region Ensemble CNN (MRE-CNN) framework for facial expression recognition, which aims to enhance the learning power of CNN models by capturing both the global a

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W. Burkhardtsmaier,W. Buschbeckhave been developed to discover new DTIs, whereas the prediction accuracy is not very satisfactory. Most existing computational methods are based on homogeneous networks or on integrating multiple data sources, without considering the feature associations between gene and drug data. In this paper, w

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,Fernsehübertragungen auf Leitungen,n this paper, we propose a hierarchical Convolution Neural Network (Hier-CNN) for emotion cause detection. Our Hier-CNN model deals with the feature sparse problem through a clause-level encoder, and handles the less event-based information problem by a subtweet-level encoder. In the clause-level en

FEAT

https://doi.org/10.1007/978-3-642-79349-3ervation noise, which is dynamic in our marine virtual sensor task. Typically, dynamic noise is not trained directly, but approximated through terms in the loss function. Unfortunately, this noise loss function needs to be scaled by a trade-off-parameter to achieve accurate uncertainties. In this pa

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https://doi.org/10.1007/978-3-642-79349-3hey have been shown to work successfully in supervised classification and regression tasks, as well as in training unsupervised autoencoders. This work has two contributions: First, we show that dropout and dropconnect on input units, previously proposed for deep multi-layer neural networks, can als

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https://doi.org/10.1007/978-3-642-79349-3ed models in machine translation, which the models can automatically search for parts of a sentence that are relevant to a target word, we propose a multi-level attention model with maximum matching matrix rank to simulate what human does when finding a good answer for a query question. Firstly, we

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Thorsten Quandt,Jürgen Wilke,Thilo Papege text. While the state-of-the-art for this task has rapidly improved in terms of n-gram metrics, these models tend to output the same generic captions for similar images. In this work, we address this limitation and train a model that generates more diverse and specific captions through an unsuper