Results 121 to 130 of about 9,085,112 (391)
Compact Matrix Quantum Group Equivariant Neural Networks [PDF]
arXiv, 2023 We derive the existence of a new type of neural network, called a compact matrix quantum group equivariant neural network, that learns from data that has an underlying quantum symmetry. We apply the Woronowicz formulation of Tannaka-Krein duality to characterise the weight matrices that appear in these neural networks for any easy compact matrix ...
arxiv
Analysis of Neural Networks in Terms of Domain Functions [PDF]
, 2002 Despite their success-story, artificial neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as
Slump, Cees +2 more
core +2 more sources
Neural Network Operations and Susuki-Trotter evolution of Neural Network States
, 2018 It was recently proposed to leverage the representational power of artificial neural networks, in particular Restricted Boltzmann Machines, in order to model complex quantum states of many-body systems [Science, 355(6325), 2017].
Dunjko, Vedran +2 more
core +1 more source
A review of artificial intelligence in brachytherapy
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in various aspects of brachytherapy.
Jingchu Chen +4 more
wiley +1 more source
Anterior cingulate cortex and its input to the basolateral amygdala control innate fear response
Nature Communications, 2018 Brain circuits that control innate fear response are essential for an animal’s survival. Here, the authors report how the anterior cingulate cortex and its projection to amygdala control the innate fear response in mice.
Jinho Jhang +5 more
doaj +1 more source
The rebirth of neural networks [PDF]
Proceedings of the 37th annual international symposium on Computer architecture, 2010 After the hype of the 1990s, where companies like Intel or Philips built commercial hardware systems based on neural networks, the approach quickly lost ground for multiple reasons: hardware neural networks were no match for software neural networks run on rapidly progressing general-purpose processors, their application scope was considered too ...
openaire +3 more sources
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan +6 more
wiley +1 more source
Cortex Neural Network: learning with Neural Network groups [PDF]
arXiv, 2018 Neural Network has been successfully applied to many real-world problems, such as image recognition and machine translation. However, for the current architecture of neural networks, it is hard to perform complex cognitive tasks, for example, to process the image and audio inputs together. Cortex, as an important architecture in the brain, is important
arxiv
Generating Neural Networks with Neural Networks
, 2018 Hypernetworks are neural networks that generate weights for another neural network. We formulate the hypernetwork training objective as a compromise between accuracy and diversity, where the diversity takes into account trivial symmetry transformations of the target network. We explain how this simple formulation generalizes variational inference.
openaire +2 more sources