Sentence examples for hierarchical learning from inspiring English sources

Glorot et al. [86] illustrate that Deep Learning can find intermediate data representations in a hierarchical learning manner and this representation can be used for other domains.

The distributed hierarchical learning model described in this paper is based on the structure of modern software systems, where a system is decomposed into multiple subsystems.

Glorot et al. [57] demonstrate that Deep Learning is able to discover intermediate data representations in a hierarchical learning manner, and that these representations are meaningful to, and can be shared among, different domains.

The hierarchical learning architecture of Deep Learning algorithms is motivated by artificial intelligence emulating the deep, layered learning process of the primary sensorial areas of the neocortex in the human brain, which automatically extracts features and abstractions from the underlying data [4]-[6].

Deep Learning algorithms extract high-level, complex abstractions as data representations through a hierarchical learning process.

The distributed hierarchical learning model includes three agent specialisations: workers, tutors and consultants (Fig. 3).

Indeed, hierarchical feature learning using CNNs can learn specific feature representation.

The study of Barto and Mahadevan [5] discussed another hierarchical reinforcement learning cooperation approach.

Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors.

We describe an evaluation of spoken dialogue strategies designed using hierarchical reinforcement learning agents.

In this paper, we propose a hierarchical reinforcement learning architecture that realizes practical learning speed in real hardware control tasks.