An approach that combines different types of deep neural networks with probabilistic approaches to model uncertainty. Different kinds of deep learning networks, such as GANs or DRL, have shown excellent agreement in terms of their achievement and widespread application with various types of data. However, deep learning algorithms do not model uncertainty, the way Bayesian, or probabilistic approaches do. Hybrid learning models combine the two kinds to leverage the strengths of each. Some examples of hybrid models are Bayesian deep learning, Bayesian GANs, and Bayesian conditional GANs.
Comparing Hybrid Learning Models with other Models
These models maintain the dual effect of both the model from which they are hybridized; that is the reason they possess more advantages to any other model.
Hybrid Learning Models Usage
Hybrid Learning Models make it possible to expand the variety of business problems to include deep learning with uncertainty. This can help us achieve better performance and explainability of models, which in turn could encourage more widespread adoption. Expect to see more deep learning methods gain Bayesian equivalents while a combination of probabilistic programming languages starts to incorporate deep learning.
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