Data Uncertainty How are neural networks being used in the development of new energy technologies

Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ... a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process. ... information theory in the evaluation of single neuron responses and neural networks. The question of the neural code has been debated heatedly since the ... Mar 4, 2016 ... For the multiplex neural networks, we find that intra and inter couplings will complementarily cooperate to complex behaviour if the two neural ... The brain is a deep and complex recurrent neural network. The models of information processing that have dominated computational neuroscience, by contrast, are ... Feb 24, 2020 ... We show how to encode both types of domain knowledge into the widely used Gaussian scale mixture priors with Automatic Relevance Determination. ... system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an ... classification via convolutional deep neural network. In Proceedings of COLING. 2014, the 25th International Conference on Computational Linguistics ... The informational aspects of neural networks performing associative memory following Hebbian learning are analysed in detail. The recall process is decomposed ... The semantic network ROI spanned a large swathe of heteromodal cortex, including most of the aforementioned cortical areas. The neural similarity structure of ...