Sentence examples for parallel learning from inspiring English sources

Efficient parallel learning algorithms are proposed for training a powerful modular neural network, the hierarchical mixture of experts (HME).

While for large volumes of data, distributed and parallel learning methods have stronger advantages.

The training was speedup using the parallel learning algorithm described in [33] running on a Linux server with 12 cores.

They obtained a number of separation results between these models and some results on efficient parallel learning in the PAExact model.

An OTF provider will most likely receive similar requests at the same time, leading to parallel learning processes that have to be appropriately synchronized.

Following Systemic Functional Linguistics, once language learners have surpassed the intermediate proficiency plateau, they seem to follow parallel learning paths in subsequent acquisition stages regardless of the native language or languages involved (Halliday 1985; 1993; Colombi 2000, 2002, 2006; Colombi and Schleppegrell 2002; Christie 2002; Schleppegrell 2004; Achugar and Colombi 2008).

Komorowski, R. W., Manns, J. R. & Eichenbaum, H. Robust conjunctive item place coding by hippocampal neurons parallels learning what happens where.

Similar to distributed learning, another popular learning technique for scaling up traditional learning algorithms is parallel machine learning [48].

RBFNN provides a very significant tool for optimization tasks as they are extremely powerful computational devices with the capability of parallel processing, learning, generalization, and universal approximation [14, 15].

Research has also shown that students often choose to engage in multi-tasking activities in parallel with learning activities to fight boredom, entertain themselves, and stay connected to the outside world (McCoy 2013).

It has been successfully used for various parallel machine learning projects, e.g., Capuccini et al. [21] presents an MLlib-based distributed conformal prediction implementation for valid confidence estimation for large dataset problems and shows the validity and scalability of the algorithms using two large datasets.