Results 21 to 30 of about 504,050 (265)
CoRR, 2017 We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations.
Veličković, P +5 more
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Molecular signatures of attention networks
Human Brain Mapping, 2023 Abstract Attention network theory proposes three distinct types of attention—alerting, orienting, and control—that are supported by separate brain networks and modulated by different neurotransmitters, that is, norepinephrine, acetylcholine, and dopamine.
Hanna Schindler +3 more
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CoRR, 2017 Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we may want to model richer structural dependencies without abandoning end-to-end training. In this work, we experiment with incorporating richer structural distributions, encoded using graphical models ...
Yoon Kim +3 more
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Geometric Scattering Attention Networks
ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021 Geometric scattering has recently gained recognition in graph representation learning, and recent work has shown that integrating scattering features in graph convolution networks (GCNs) can alleviate the typical oversmoothing of features in node representation learning.
Yimeng Min, Frederik Wenkel, Guy Wolf
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A Regularized Attention Mechanism for Graph Attention Networks [PDF]
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020 Machine learning models that can exploit the inherent structure in data have gained prominence. In particular, there is a surge in deep learning solutions for graph-structured data, due to its wide-spread applicability in several fields. Graph attention networks (GAT), a recent addition to the broad class of feature learning models in graphs, utilizes ...
Uday Shankar Shanthamallu +2 more
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Attention to Emotional Information Is Associated With Cytokine Responses to Psychological Stress
Frontiers in Neuroscience, 2018 This study aimed to investigate the impact of mental stress on salivary cytokines and attention to emotional stimuli, as well as associations between stress-induced changes of immune and cognitive parameters.
Viktoriya Maydych +3 more
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Modeling Graph Neural Networks and Dynamic Role Sorting for Argument Extraction in Documents
Applied Sciences, 2023 The existing methods for document-level event extraction mainly face two challenges. The first challenge is effectively capturing event information that spans across sentences.
Qingchuan Zhang +4 more
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CoRR, 2018 We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks.
Çaglar Gülçehre +10 more
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Attentional Networks in Parkinson’s Disease [PDF]
Behavioural Neurology, 2013 We tested the efficiency of three attentional systems (spatial orienting, phasic alerting and executive control) in patients with Parkinson's disease (PD), by using a modified version of the Attention Network Test, which employs acoustic tones to modulate phasic alertness.
Cristinzio, Chiara +4 more
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Imaging attention networks [PDF]
NeuroImage, 2012 The study of attention has largely been about how to select among the various sensory events but also involves the selection among conflicting actions. Prior to the late 1980s, locating bottlenecks between sensory input and response dominated these studies, a different view was that attentional limits involved the importance of maintaining behavioral ...
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