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

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DTI-RCNN: New Efficient Hybrid Neural Network Model to Predict Drug–Target Interactionshave 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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表否定

Direct Training of Dynamic Observation Noise with UMarineNetervation 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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恃強凌弱

A Multi-level Attention Model for Text Matchinged 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

構(gòu)想

Attention Enhanced Chinese Word Embeddingsof existing word representation methods, we improve CBOW in two aspects. Above all, the context vector in CBOW is obtained by simply averaging the representation of the surrounding words while our AWE model aligns the surrounding words with the central word by global attention mechanism and self att

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Balancing Convolutional Neural Networks Pipeline in FPGAss. However, their processing power demand offers a challenge to their implementation in embedded real-time applications. To tackle this problem, we focused in this work on the FPGA acceleration of the convolutional layers, since they account for about 90% of the overall computational load. We implem

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愉快嗎

https://doi.org/10.1007/978-3-030-01418-6artificial intelligence; classification; clustering; computational linguistics; computer networks; Human-

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978-3-030-01417-9Springer Nature Switzerland AG 2018