Relative regularization coefficients: ARTM & TopicNet using

By reading this tutorial you will know how to use TopicNet and ARTM libraries for building topic models with regularizers.

Why do we need regularizers?

What about the relative coefficients of regularization?

How do we use them?

If we designate parameter λ while announcing regularizer, τ will show the relative degree of regularization, not an absolute one.

ARTM using

artm.DecorrelatorPhiRegularizer(name=’DecorrelatorPhi’, gamma=0, tau=2, …)

Set the decorrelation regularizer which will affect the model twice as strong as the text (with equal strength for all topics).

TopicNet using

rel_toolbox_lite.handle_regularizer

This method takes your regularizer, model and some other things and transforms the regularization coefficient from absolute to relative by the above formula.

Congratulations, now you are a happy possessor of secret knowledge — relative regularization coefficients in topic models!

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Machine Intelligence Laboratory

MIL. Team is the united and professional group of researchers, developers and engineers conducting R&D projects in the field of Artificial Intelligence.