Compositional Deep Learning

The inability of Deep Learning to perform compositional learning is one of the main reasons for Deep Learning's most critical limitations, including the need to feed them tons of data. Compositionality is the algebraic capacity to understand and produce novel combinations from known components (Loula 2018). While the human brain can easily learn compositionally, Neural Networks (NNs) are not able to discover and store skills that are common across problems, and to re-combine them in a hierarchical fashion to solve new challenges (Liška 2018). The human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before the human language learning enjoys a good kind of combinatorial explosion -- if a person knows the meaning of "to run" and that of "slowly", she can immediately understand what it means "to run slowly", even if she has never uttered or heard this expression before (Loula 2018). This principle helps to explain how, when acquiring a language, we can quickly bootstrap to a potentially infinite number of expressions from very limited training data (Loula 2018).