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It is challenging for existing geodesic path algorithms to process large-scale models with millions of vertices.
Our experimental results show that our large-scale implementation of the L-BFGS algorithm can easily scale from training models with millions of parameters to models with billions of parameters by simply increasing the number of commodity computational nodes.
The rise of big data has led to new demands for machine learning (ML) systems to learn complex models, with millions to billions of parameters, that promise adequate capacity to digest massive datasets and offer powerful predictive analytics (such as high-dimensional latent features, intermediate representations, and decision functions) thereupon.
The multi-scale nature of the electrophysiology problem makes difficult its numerical solution, requiring temporal and spatial resolutions of 0.1 ms and 0.2 mm respectively for accurate simulations, leading to models with millions degrees of freedom that need to be solved for thousand time steps.
A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters.
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Table 4 shows a comparison of the 1st through 89th minute TAC GDC models with 5th through 89th minute TAC washout models.
[ 22] I first estimated each categorical model with 1st order marginal quasi-likelihood (MQL) estimation and IGLS to obtain starting values.
Therefore, two separate multivariate models, one with 7th and the other with 6th edition system, were run to avoid problems with presence of multicollinearity.
A multivariable model with both 6th and 7th edition criteria included was used to assess the remaining value of the 6th edition when the 7th edition information was known.
On a conceptual level, these models (1st and 2nd order) do not fit in with the idea of frailty.
Natural spline models (with knots at the 20th, 50th, and 80th percentiles) as well as linear models were fitted to the data to investigate the shape of the exposure response relation.
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systems with millions
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