Results 11 to 20 of about 502,069 (324)
Scholarpedia, 2008 The(frequently updated) original version is avalable at http://www.scholarpedia.org/article ...
Peter Földiák, Dominik M. Endres
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Magnetic-Sparseness and Schrödinger Operators on Graphs [PDF]
Annales de l'Institute Henri Poincare. Physique theorique, 2017 We study magnetic Schrödinger operators on graphs. We extend the notion of sparseness of graphs by including a magnetic quantity called the frustration index.
M. Bonnefont +4 more
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Estimating optimal sparseness of developmental gene networks using a semi-quantitative model. [PDF]
PLoS ONE, 2017 To estimate gene regulatory networks, it is important that we know the number of connections, or sparseness of the networks. It can be expected that the robustness to perturbations is one of the factors determining the sparseness.
Natsuhiro Ichinose +2 more
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The Relationship between Sparseness and Energy Consumption of Neural Networks
Journal of Neural Transplantation and Plasticity, 2020 About 50-80% of total energy is consumed by signaling in neural networks. A neural network consumes much energy if there are many active neurons in the network. If there are few active neurons in a neural network, the network consumes very little energy.
Guanzheng Wang +3 more
semanticscholar +2 more sources
Augmented Collaborative Filtering for Sparseness Reduction in Personalized POI Recommendation [PDF]
ACM Transactions on Intelligent Systems and Technology, 2017 As mobile device penetration increases, it has become pervasive for images to be associated with locations in the form of geotags. Geotags bridge the gap between the physical world and the cyberspace, giving rise to new opportunities to extract further ...
C. Cui +4 more
semanticscholar +2 more sources
Adaptive Sparseness for Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003 The goal of supervised learning is to infer a functional mapping based on a set of training examples. To achieve good generalization, it is necessary to control the "complexity" of the learned function.
Mário A. T. Figueiredo
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Proportionate NSAF Algorithms With Sparseness-Measured for Acoustic Echo Cancellation
AEU - International Journal of Electronics and Communications, 2017 Yi Yu
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Sparse Convolution for Approximate Sparse Instance
CoRR, 2023 Computing the convolution $A \star B$ of two vectors of dimension $n$ is one of the most important computational primitives in many fields. For the non-negative convolution scenario, the classical solution is to leverage the Fast Fourier Transform whose time complexity is $O(n \log n)$.
Xiaoxiao Li +2 more
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Dissipativity Analysis of Large-Scale Networked Systems
Applied Sciences, 2023 This paper investigates the dissipativity analysis of large-scale networked systems with linear time-invariant dynamics. The networked system is composed of a large number of subsystems whose connections are arbitrary, and each subsystem can have ...
Yuanfei Sun, Jirong Wang, Huabo Liu
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Frontiers in Human Neuroscience, 2014 Entrained behavior coordinates, predicts, and modulates multi-scale rhythmic gestures with high spatio-temporal precision even as it shows flexible adaptation in response to perturbation (Clayton et al., 2005; Altenmuller et al., 2006; Phillips-Silver et al., 2010).
Eric eBarnhill, Eric eBarnhill
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