Current Advances in Neural Networks
Víctor Gallego, David Rı́os Insua
Abstract
This article reviews current advances and developments in neural networks. This requires recalling some of the earlier work in the field. We emphasize Bayesian approaches and their benefits compared to more standard maximum likelihood treatments. Several representative experiments using varied modern neural architectures are presented.
Topics & Concepts
Artificial neural networkComputer scienceCurrent (fluid)Deep neural networksBayesian probabilityField (mathematics)Artificial intelligenceMachine learningEngineeringMathematicsElectrical engineeringPure mathematicsNeural Networks and ApplicationsAnomaly Detection Techniques and ApplicationsMachine Learning and Data Classification