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Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series
Deep Learning is a consolidated, state-of-the-art Machine Learning tool ...
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Deep learning systems as complex networks
Thanks to the availability of large scale digital datasets and massive a...
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Hiding Information in Big Data based on Deep Learning
The current approach of information hiding based on deep learning model ...
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Automated Architecture Design for Deep Neural Networks
Machine learning has made tremendous progress in recent years and receiv...
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GEN Model: An Alternative Approach to Deep Neural Network Models
In this paper, we introduce an alternative approach, namely GEN (Genetic...
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Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning
Learning the underlying patterns in the data goes beyond instance-based ...
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Do Deep Learning Models Have Too Many Parameters? An Information Theory Viewpoint
Deep learning models often have more parameters than observations, and s...
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Learning Representations from Deep Networks Using Mode Synthesizers
Deep learning Networks play a crucial role in the evolution of a vast number of current machine learning models for solving a variety of real world non-trivial tasks. Such networks use big data which is generally unlabeled unsupervised and multi-layered requiring no form of supervision for training and learning data and has been used to successfully build automatic supervisory neural networks. However the question still remains how well the learned data represents interestingness, and their effectiveness i.e. efficiency in deep learning models or applications. If the output of a network of deep learning models can be beamed unto a scene of observables, we could learn the variational frequencies of these stacked networks in a parallel and distributive way.This paper seeks to discover and represent interesting patterns in an efficient and less complex way by incorporating the concept of Mode synthesizers in the deep learning process models
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