Results 131 to 140 of about 9,085,112 (391)
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Introduction Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a misalignment between AI systems and their users.
Luca M. Heising +11 more
wiley +1 more source
Parametrical Neural Networks and Some Other Similar Architectures [PDF]
arXiv, 2006 A review of works on associative neural networks accomplished during last four years in the Institute of Optical Neural Technologies RAS is given. The presentation is based on description of parametrical neural networks (PNN). For today PNN have record recognizing characteristics (storage capacity, noise immunity and speed of operation).
arxiv
Learning text representation using recurrent convolutional neural network with highway layers [PDF]
, 2016 Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers ...
Luo, Rui +3 more
core +1 more source
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Background Tumor segmentation is crucial for lung disease diagnosis and treatment. Most existing deep learning‐based automatic segmentation methods rely on manually annotated data for network training. Purpose This study aims to develop an unsupervised tumor segmentation network smic‐GAN by using a similarity‐driven generative adversarial ...
Chengyijue Fang +2 more
wiley +1 more source
Frontiers in Computational Neuroscience, 2018 Humans have flexible control over cognitive functions depending on the context. Several studies suggest that the prefrontal cortex (PFC) controls this cognitive flexibility, but the detailed underlying mechanisms remain unclear.
Satoshi Kuroki +2 more
doaj +1 more source
Combining Recurrent and Convolutional Neural Networks for Relation Classification [PDF]
arXiv, 2016 This paper investigates two different neural architectures for the task of relation classification: convolutional neural networks and recurrent neural networks. For both models, we demonstrate the effect of different architectural choices. We present a new context representation for convolutional neural networks for relation classification (extended ...
arxiv
On the Kolmogorov neural networks
Neural Networks14 pages, 1 figure; this article uses material from arXiv:2012 ...
Aysu Ismayilova, Vugar E. Ismailov
openaire +5 more sources
Neural Networks, 2019 It has been known for discrete-time recurrent neural networks (NNs) that binary-state models using the Heaviside activation function (with Boolean outputs 0 or 1) are equivalent to finite automata (level 3 in the Chomsky hierarchy), while analog-state NNs with rational weights, employing the saturated-linear function (with real-number outputs in the ...
openaire +4 more sources
, 2020 We present simplicial neural networks (SNNs), a generalization of graph neural networks to data that live on a class of topological spaces called simplicial complexes. These are natural multi-dimensional extensions of graphs that encode not only pairwise relationships but also higher-order interactions between vertices - allowing us to consider richer ...
Michaël Defferrard +2 more
openaire +2 more sources
Relationship of cognitive decline with glucocerebrosidase activity and amyloid‐beta 42 in DLB and PD
Annals of Clinical and Translational Neurology, EarlyView.Abstract Objective Dementia with Lewy bodies (DLB) and Parkinson's disease (PD) share clinical, pathological, and genetic risk factors, including GBA1 and APOEε4 mutations. Biomarkers associated with the pathways of these mutations, such as glucocerebrosidase enzyme (GCase) activity and amyloid‐beta 42 (Aβ42) levels, may hold potential as predictive ...
Maria Camila Gonzalez +15 more
wiley +1 more source