Data Dependency

Aug 3, 2021 ... In our study the hypothesis is tested on data taken from Warsaw Stock Exchange in 2007–2009 years. The hypothesis is tested by predictive ... Sep 10, 2019 ... Using a genetic algorithm approach to find optimal neural network structures for model-free policy learning in reinforcement learning; Using ... Jun 23, 2020 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure. We present a review on the recent advances and emerging opportunities around the theme of analyzing deep neural networks (DNNs) with information-theoretic ... Nov 23, 2010 ... ... information processing by neural networks, using a simple cultured neural network. 5. Conclusion. Many useful paradigms and engineering ... It is an intrinsic phenomenon unrelated to cardiac rhythm or neural and hormonal regulation. Vasomotion is found to be particularly prominent under conditions ... In this work we study the distributed representations learnt by generative neural network models. In particular, we investigate the properties of redundant and ... Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ... ... information transmission by single neurons but not by the entire network. Most surprisingly, addition of a delay can change the dependence of the information ... Feb 22, 2023 ... We compare this control to a regularly-trained neural network, and show that perturbations on nodes carrying relay information cause failures in ...