Data Augmentation

Dec 31, 2022 ... We relax these limitations using deep learning methods. We use a neural network to classify insurees into joint choice of coverage and claim ... ... system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an ... Keywords: Feedforward neural network, complexity, information complexity, neural complexity, radial basis functions, RBF networks, learning. 1. Introduction. Dec 19, 2019 ... Abstract: Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. Feb 26, 2024 ... This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more ... View details for Real-time Twitter data sentiment analysis to predict the recession in the UK using Graph Neural Networks. ... neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of ... Sep 4, 2023 ... ... neuronal networks or whether it is related to informational load. ... To explore this topic, we used an in vitro neural network of cortical ... Mar 24, 2024 ... Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical ... information carried by the network via neural codes. 1.3. Consciousness and Informational Complexity. The idea of computational mod- els of consciousness ...