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These populations are a heterogeneous mix of architectures, weights, and input variables which undergo mating, crossover, and recombination to ultimately identify an optimum NN solution, simultaneously finding influential SNPs and fitting networks weights.
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Here, we investigate a variety of topological properties of RIGs to find a well fitting network null model for them.
The estimation of network model parameters has been an incidental aspect of our work; however, it is related to the quite different problem of fitting network models to known biological networks.
To find a best fitting network, the space of possible network hypotheses is searched by a GA.
Only a well-fitting network model that precisely reproduces the network structure and laws through which the network has emerged can enable us to understand and replicate the underlying biological processes.
To avoid over-fitting, network weights are saved as intermediate files at different stages of training.
However, for almost all network comparison tasks, there is no prior information on the structure of the compared networks, and consequently, a well-fitting network null model for these networks is unknown.
This is because a well-fitting network null model is hard to determine and differs for most real-world networks and therefore, it is not possible to choose a theoretically well-founded gold-standard network for NetDis comparison of real-world networks, especially because each of the compared networks might require a different gold-standard network.
It's clear that comparing the well fitted network properties mentioned above is not sufficient to identify the best-fitting model.
This method had utilized alternative tree based crossover, back propagation for locally fitting neural network weights.
We employed an alternative tree-based crossover, backpropagation for locally fitting neural network weights, and incorporation of domain knowledge obtainable from publicly accessible biological databases for initializing the search for gene-gene interactions.
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