Backpropagation

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. in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ... 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 ... The brain is a deep and complex recurrent neural network. The models of information processing that have dominated computational neuroscience, by contrast, are ... Feb 6, 2025 ... Abstract and Figures. While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is ... We do so by performing experiments on artificial neural networks in the context of fairness in machine learning, where the goal is to induce fairness in the ... 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 ... neuron control with light. This technology is called optogenetics. [Photo ] The most effective means to analyze information processing in the neural network is ... ... information theory in the evaluation of single neuron responses and neural networks. The question of the neural code has been debated heatedly since the ...