Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2020; 29th International C Igor Farkaš,Paolo Masulli,Stefan Wermter Conference proc

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Convolutional Neural Networks with Reusable Full-Dimension-Long Layers for Feature Selection and Clanetwork. Despite all the successes of deep learning, neural networks of significant depth could not ensure better performance compared to shallow architectures. The approach presented in the article employs this idea, making use of yet shallower, but productive architecture. The main idea of the pro

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Compressing Genomic Sequences by Using Deep Learningstorage, processing, and transmission. Standard compression tools designed for English text are not able to compress genomic sequences well, so an effective dedicated method is needed urgently. In this paper, we propose a genomic sequence compression algorithm based on a deep learning model and an a

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Tucker Tensor Decomposition of Multi-session EEG Dataram (EEG) is rare and often without detailed electrophysiological interpretation of the obtained results. In this work, we apply the Tucker model to a set of multi-channel EEG data recorded over several separate sessions of motor imagery training. We consider a three-way and four-way version of the

Organonitrile

Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process D improve the quality of such predictions, we propose a Bayesian inference architecture that enables the combination of multiple sources of sensory information with an accurate and flexible model for the online prediction of high-dimensional kinematics. Our method integrates hierarchical Gaussian pro

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Conference proceedings 202049 submissions. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action...*The conference was postponed to 2021 due to the COVID-19 pandemic..

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