Sentence examples for model learning framework from inspiring English sources

In previous work we first proposed a pilot system EQML [25,26], an evolutionary qualitative model learning framework.

Experimental results have shown that our proposed integrative qualitative and quantitative model learning framework is feasible and effective in the presence of incomplete knowledge and qualitative data.

The above facts motivate us to develop an integrative qualitative and quantitative model learning framework, and we expect that by making use of the advantages of both learning approaches better learning performance will be achieved to assist wet-laboratory research.

This indicates that our proposed model learning framework can effectively optimise the kinetic rates associated with biochemical reactions, which in turn drive the species behaviours of the synthetic models to approach the target species behaviours in an approximate manner.

We point out that our model learning framework can be applied in both the context of computational systems biology for biochemical system identification and synthetic biology for the modular design of desired biochemical systems.

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology.

By using machine learning techniques like ensemble models and multi-armed bandit methods, Clipper can select and combine predictions from multiple models, even models trained in different machine learning frameworks, to provide more accurate and robust predictions.

Machine learning frameworks like Google's TensorFlow and Caffe help people create deep learning models without the rigorous skill-set of a machine learning specialist.

Machine learning frameworks like Google's TensorFlow and Caffe help people create deep learning models without the rigorous skill-set of a machine learning specialist.

Interposed between end-user applications and a wide range of machine learning frameworks, Clipper introduces a modular architecture to simplify model deployment across frameworks.

Given experience with a set of objects, our framework learns a causal model for each object and a causal schema that captures commonalities among these causal models.