Lack of Robustness How can we build more robust and reliable neural networks

Apr 17, 2019 ... ... learning (AL) framework to select most informative samples for training our model using a Bayesian neural network. Informative samples are ... Jun 29, 2018 ... The brain has numerous levels of interaction ranging from gene networks that control cell function to neural circuits that control behavior. neuron control with light. This technology is called optogenetics. [Photo ] The most effective means to analyze information processing in the neural network is ... Informational capacity and recall quality in sparsely encoded Hopfield-like neural network: analytical approaches and computer simulation Mar 24, 2024 ... Graph neural networks (GNNs) have achieved state-of-the-art results on many graph representation learning tasks by exploiting statistical ... Neural networks can realize conditional distributions of the form p(y. z I x. u) specified by network parameter u. The set of such realizable distributions is a ... Aug 9, 2024 ... Neural networks are modeled after the human brain's structure and function. A neural network consists of interconnected layers of nodes ( ... Feb 26, 2024 ... This article introduces a quantitative approach to modeling the cost of control in a neural network architecture when it is required to execute one or more ... Jun 23, 2020 ... In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure. ABSTRACT. Introduction: Natural Language Processing through artificial neural networks has gaps that can be addressed by Information Science.