INFINITELY MANY SOLUTIONS TO THE NEUMANN PROBLEM FOR ELLIPTIC EQUATIONS INVOLVING THE p-LAPLACIAN AND WITH DISCONTINUOUS NONLINEARITIES [PDF]
, 2002 Pasquale Candito
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Existence Results for the p-Laplacian with Nonlinear Boundary Conditions
, 2001 Julián Fernández Bonder +1 more
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CAAI Transactions on Intelligence Technology, EarlyView.Abstract Graph neural networks (GNNs) have revolutionised the processing of information by facilitating the transmission of messages between graph nodes. Graph neural networks operate on graph‐structured data, which makes them suitable for a wide variety of computer vision problems, such as link prediction, node classification, and graph classification.
Amit Sharma +4 more
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Two generalized Lyapunov-type inequalities for a fractional p-Laplacian equation with fractional boundary conditions. [PDF]
J Inequal Appl, 2017 Liu Y, Xie D, Yang D, Bai C, Bai C.
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Fredholm Alternative for the p-Laplacian in Higher Dimensions
, 2001 Pavel Drábek, Gabriela Holubová
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Enhancing generalized spectral clustering with embedding Laplacian graph regularization
CAAI Transactions on Intelligence Technology, EarlyView.Abstract An enhanced generalised spectral clustering framework that addresses the limitations of existing methods by incorporating the Laplacian graph and group effect into a regularisation term is presented. By doing so, the framework significantly enhances discrimination power and proves highly effective in handling noisy data.
Hengmin Zhang +5 more
wiley +1 more source
Nonlinear diffusion with the p-Laplacian in a Black-Scholes-type model
Electronic Journal of Differential Equations, 2023 Peter Takac
doaj
Equations with a $p$-Laplacian and an asymmetric nonlinear term
, 2001 C. Fabry, Raúl Manásevich
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Existence theory for single and multiple solutions to singular positone boundary value problems for the delay one-dimensional p-Laplacian [PDF]
, 2003 Daqing Jiang +3 more
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Boosted unsupervised feature selection for tumor gene expression profiles
CAAI Transactions on Intelligence Technology, EarlyView.Abstract In an unsupervised scenario, it is challenging but essential to eliminate noise and redundant features for tumour gene expression profiles. However, the current unsupervised feature selection methods treat all samples equally, which tend to learn discriminative features from simple samples.
Yifan Shi +5 more
wiley +1 more source