Computational Efficiency

... informational differences among them, efficiently coordinating and integrating the sub-prediction results. Secondly, a two-stage predictive model selection ... Aug 30, 2023 ... To explore this topic, we used an in vitro neural network of cortical neurons that was trained to play a simplified game of 'Pong' to ... in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ... The human brain is often used as an analogy to explain neural networks, however this is not particularly helpful as it implies machines understand information ... Jan 16, 2021 ... ... informational and non-informational classes, respectively ... The deep neural network (DNN) classifier obtained the highest accuracy (95.2%). Jan 29, 2024 ... We will see how ANN-assisted mixture space exploration can adapt to dynamic mixtures and balance the local and global information. Section 3 ... Sat 7:10 a.m. - 7:18 a.m.. Neural networks learn an environment's geometry in latent space by performing predictive coding on visual scenes ( Oral ) > link Neural networks integrating information and primary reward. Early neuroscience studies of information seeking in monkeys focused on the “reward prediction error ... Oct 6, 2022 ... Our results show that synergy increases as neural networks learn multiple diverse tasks. Furthermore, performance in tasks requiring integration ... Jun 23, 2020 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure.