Transparency What is explainable AI (XAI) and why is it important for neural networks

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 ... Jun 23, 2020 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure. ... informational differences among them, efficiently coordinating and integrating the sub-prediction results. Secondly, a two-stage predictive model selection ... performing the functions of associative memory. L. Figure 1. Dependence of the main neural network informational charaaeristi~ on the number of stored templates ... Jan 9, 2024 ... 43. Francis, N.A. ... Sequential transmission of task-relevant information in cortical neuronal networks. Cell Rep. 2022; 39, 110878. Sep 22, 2020 ... By recruiting adjacent neurons into oscillations, waves of synchronously firing neuronal networks—and thereby conscious thoughts—can travel ... Data-driven network performance prediction for B5G networks: a graph neural network approach. Yaqoob, M., Trestian, R. and Nguyen, H. 2022. Data-driven ... The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the ... 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 ... Modern artificial intelligence (AI) approaches mainly rely on neural network (NN) or deep NN methodologies. However, these approaches require large amounts ...