Results 81 to 90 of about 852,410 (222)
Deep Neural Networks - A Brief History [PDF]
Introduction to deep neural networks and their history.
arxiv
Assessing Intelligence in Artificial Neural Networks [PDF]
The purpose of this work was to develop of metrics to assess network architectures that balance neural network size and task performance. To this end, the concept of neural efficiency is introduced to measure neural layer utilization, and a second metric called artificial intelligence quotient (aIQ) was created to balance neural network performance and
arxiv
Abstract Purpose This study aims to develop a CycleGAN based denoising model to enhance the quality of low‐dose PET (LDPET) images, making them as close as possible to standard‐dose PET (SDPET) images. Methods Using a Philips Vereos PET/CT system, whole‐body PET images of fluorine‐18 fluorodeoxyglucose (18F‐FDG) were acquired from 37 patients to ...
Yang Liu, ZhiWu Sun, HaoJia Liu
wiley +1 more source
Geometric Decomposition of Feed Forward Neural Networks [PDF]
There have been several attempts to mathematically understand neural networks and many more from biological and computational perspectives. The field has exploded in the last decade, yet neural networks are still treated much like a black box. In this work we describe a structure that is inherent to a feed forward neural network.
arxiv
Progressive Myoclonus Epilepsy: Distinctive MRI Changes in Cerebellar and Motor Networks
ABSTRACT Objective Progressive myoclonus epilepsy (PME) is a rare generalized epilepsy syndrome with a well‐characterized genetic basis. The brain networks that are affected to give rise to the distinctive symptoms of PME are less well understood. Methods Eleven individuals with PME with a confirmed genetic diagnosis and 22 controls were studied.
Jillian M. Cameron+3 more
wiley +1 more source
On the computational efficiency of symmetric neural networks
AbstractAn open problem concerning the computational power of neural networks with symmetric weights is solved. It is shown that these networks possess the same computational power as general networks with asymmetric weights; i.e., these networks can compute any recursive function.
openaire +2 more sources
Claustrum Volume Is Reduced in Multiple Sclerosis and Predicts Disability
ABSTRACT Objective The claustrum is a small, thin structure of predominantly gray matter with broad connectivity and enigmatic function. Little is known regarding the impact of claustrum pathology in multiple sclerosis (MS). Methods This study assessed whether claustrum volume was reduced in MS and whether reductions were associated with specific ...
Nicole Shelley+5 more
wiley +1 more source
Implementation of intelligent model for pneumonia detection
The advancement of technology in the field of artificial intelligence and neural networks allows us to improve speed and efficiency in the diagnosis of various types of problems.
Željko Knok*+2 more
doaj +1 more source
Early Language Impairment as an Integral Part of the Cognitive Phenotype in Huntington's Disease
ABSTRACT Objective Huntington's disease (HD) speech/language disorders have typically been attributed to motor and executive impairment due to striatal dysfunction. In‐depth study of linguistic skills and the role of extrastriatal structures in HD is scarce.
Arnau Puig‐Davi+13 more
wiley +1 more source
Generalized Projective Synchronization between Two Different Neural Networks with Mixed Time Delays
The generalized projective synchronization (GPS) between two different neural networks with nonlinear coupling and mixed time delays is considered. Several kinds of nonlinear feedback controllers are designed to achieve GPS between two different such ...
Xuefei Wu+4 more
doaj +1 more source