Training on Multiple Devices

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 ... 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 ... Mar 24, 2024 ... Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical ... 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. Dec 19, 2019 ... Abstract: Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. Classically, external receptors of the body transmit information from the environment to the cerebral cortex via the thalamus. This review explains and ... ... neural networks contribute to familiar face recognition. Original language, English. Article number, 117896. Pages (from-to), 1-15. Number of pages, 15. Journal ... ... neural representation of familiar faces and the degree to which peri-frontal neural networks contribute to familiar face recognition. He also gives an example using a neural network class with fixed architecture as the hypothesis class. He is able to predict the qualitative effects of ... 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 ...