Dynamic Graphs

Abstract. The concept of Learning by Stimulus Avoidance (LSA) has been proposed in recent literature, and the methods of avoiding stimuli: action, prediction, ... Dec 20, 2024 ... In ecosystems, financial markets, and neural networks, entropy fluctuates as new information reshapes the probability landscape. 4.2 The ... Dec 19, 2019 ... Abstract: Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks. 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 ... The semantic network ROI spanned a large swathe of heteromodal cortex, including most of the aforementioned cortical areas. The neural similarity structure of ... in the upper network level. Moreover, in a slightly different direction, research on multi-task learning neural networks, an approach aiming at internal ... Sep 5, 2024 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure in ... Sep 11, 2020 ... Alternatively, it is. 10. Page 29. 2.1. Preliminaries on convolutional neural networks often found that adding depth to the network by stacking ... Jan 9, 2024 ... 43. Francis, N.A. ... Sequential transmission of task-relevant information in cortical neuronal networks. Cell Rep. 2022; 39, 110878. We propose a novel neural network architecture which trains spatial positions of neural soma and axon pairs, where weights are calculated by axon-soma ...