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Built upon it, the second phase is an interactive exploration of tree models via a few user clicks, where a user intent evaluation model is learned online to guide the modeling process.
The inference model is learned from the on-the-shelf training images without any occlusion.
This model is learned based on live imaging of cellularization and gastrulation, two highly stereotyped morphogenetic processes at this stage of embryogenesis.
The CRF model is learned based on sparse features of local patches in a multi-scale structure and it also takes the contextual information of target into consideration.
BOA is an Estimation of Distribution Algorithm in which a Bayesian network (as a probabilistic model) is learned from the population and then sampled to generate new solutions.
A similar approach was proposed by Khan et al. [62], where the local model is learned from the facial region.
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The phasing model is learnt using an analysis-synthesis loop that iterates HMM estimations and forced alignments with the original data.
The parameters of this model are learned from a set of demonstrations performed by a human.
The parameters for each model are learned in an unsupervised fashion as the robot experiments with its arm over a period of four minutes.
The hyperparameters in the probabilistic model are learned using sequential Monte Carlo (SMC) method, which is superior to standard Markov chain Monte Carlo (MCMC) methods for multi-modal distributions.
The parameters of the model are learned using a weakly supervised bootstrapping approach, without the need for manually tuned parameters or any other language expertise.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com