They argue that linguistic competence is derived from and informed by language use, performance, taking the directly opposite view to the generative model.

As each new layer is added the overall generative model gets better.

These methods can roughly be divided into two categories, generative and discriminative models.

For generative models, relative positions of codewords are also taken into account.

Interest in inductive learning using generative models also began in the 1970s.

Most models of sparse coding are based on the linear generative model.

A conditional distribution can be formed from a generative model through Bayes' rule.

They don't necessarily perform better than generative models at classification and regression tasks.

As such, it constitutes a bridge between generative and probabilistic models of documents.

The problem of defining a generative model for object recognition is difficult.