Results 151 to 160 of about 7,621 (191)

On the mathematical structure and numerical solution of discrete-continuous optimization problems in DDCM. [PDF]

Z Angew Math Phys
Gebhardt CG, Lange S, Steinbach MC.
europepmc   +1 more source

Development of transcriptomic tools for predicting the response to individual drug of the mFOLFIRINOX regimen in patients with metastatic pancreatic cancer. [PDF]

Front Oncol
Fraunhoffer N, Teyssedou C, Pessaux P, Bigonnet M, Dusetti N, Iovanna J.   +5 more
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Blockchain enabled collective and combined deep learning framework for COVID19 diagnosis. [PDF]

Sci Rep
Periyasamy S, Kaliyaperumal P, Thirumalaisamy M, Balusamy B, Elumalai T, Meena V, Jadoun VK.   +6 more
europepmc   +1 more source

Stroke recovery beyond initial severity: The complementary roles of brain structure and brain function in acute stroke

Zich C, Mardell LC, Quinn AJ, Bestmann S, Ward NS.   +4 more
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Robust and replicable effects of ageing on resting state brain electrophysiology measured with MEG

Quinn AJ, Pitt J, Kohl O, Gohil C, van Es MW, Nobre AC, Woolrich MW.   +6 more
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Blockwise Recursive Moore–Penrose Inverse for Network Learning

IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022
Training neural networks with the Moore–Penrose (MP) inverse has recently gained attention in view of its noniterative training nature. However, a significant drawback of learning based on the MP inverse is that the computational memory consumption grows
Huiping Zhuang, Zhiping Lin, Kar-Ann Toh
semanticscholar   +1 more source

Weighted generalized Moore–Penrose inverse

Georgian Mathematical Journal, 2023
The aim of this paper is to present the weighted generalized Moore–Penrose inverse of an operator between two Hilbert spaces as an extension of the Moore–Penrose inverse and the generalized Moore–Penrose inverse defined for an operator on a Hilbert space.
D. Mosić
semanticscholar   +1 more source

Multimodal Moore–Penrose Inverse-Based Recomputation Framework for Big Data Analysis

IEEE Transactions on Neural Networks and Learning Systems, 2022
Most multilayer Moore–Penrose inverse (MPI)-based neural networks, such as deep random vector functional link (RVFL), are structured with two separate stages: unsupervised feature encoding and supervised pattern classification.
Wandong Zhang, Yimin Yang, Q. M. J. Wu, Tianlei Wang, Hui Zhang   +4 more
semanticscholar   +1 more source