A two-step spectral embedding procedure that removes irrelevant components from a knowledge matrix then projects to recover shared and heterogeneous signals for rare-disease clinical concept and patient embeddings.
& Lederer, J.: How many samples are needed to train a deep neural network?arXiv preprint, arXiv:2405.16696 (2024)
2 Pith papers cite this work. Polarity classification is still indexing.
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Empirical study on a robotic manipulator concludes that training sets larger than 125 samples yield no further gains in accuracy or efficiency for feedforward neural network inverse kinematics solvers.
citing papers explorer
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Enhancing Spectral Embedding through Robust and Flexible Knowledge Transfer in Electronic Health Records
A two-step spectral embedding procedure that removes irrelevant components from a knowledge matrix then projects to recover shared and heterogeneous signals for rare-disease clinical concept and patient embeddings.
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How Many Training Samples Are Needed for the Inverse Kinematics Solutions by Artificial Neural Networks
Empirical study on a robotic manipulator concludes that training sets larger than 125 samples yield no further gains in accuracy or efficiency for feedforward neural network inverse kinematics solvers.