Learning Complex Features

... information theory in the evaluation of single neuron responses and neural networks. The question of the neural code has been debated heatedly since the ... 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. May 9, 2023 ... The article examines the role of the spontaneous activity of neurons, and therefore, of brain neural networks, in information processing. The recent surge of graph embedding and graph neural networks also call for renewed and novel methodologies for heterogeneous information network analysis. As a ... Sep 5, 2024 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure in ... The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the ... ... neural coding models that depend on phase, increasing the informational content of neural representations; and (4) likely originate from the dynamics of ... 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. May 29, 2024 ... However, in large systems, such as neuronal networks, the reconstruction of such topology is usually carried out from the information encoded in ... ... Learning Capability of Deep Neural Networks: A Positivist Perspective Jaebong Son and Chang Heon Lee. PDF · The Sky Is the Limit: A Review of Drone Research ...