Sentence examples for model can quickly from inspiring English sources

But in today's world, leaders using the network model can quickly outpace those who remain focused on winning individual battles.

The results showed that the developed model can quickly and accurately draw three-dimensional and two-dimensional structural diagrams of bits, not only standardizing the designing flowchart, but also improving the quality and efficiency as well as reducing the error of drawings.

Observing the snapshots in Fig. 3 and referring to the outlierness indicator in Fig. 4 show that the model can quickly adapt to the novelty: 1.

For example, in the case of bankruptcy (a strong signal used in underwriting decisions), a machine learning model can quickly calculate the number of years since a person filed bankruptcy, but wouldn't know that people are only allowed to declare bankruptcy once every seven years.

For example, in the case of bankruptcy (a strong signal used in underwriting decisions), a traditional underwriting model can quickly calculate the number of years since a person filed bankruptcy, but wouldn't know that people are only allowed to declare bankruptcy once every seven years.

These preevent/event/postevent phase challenges and the organizational flexibility requirements of an all-hazards response model can quickly become overwhelming for public health departments.

These models can quickly predict the reaction rate and estimate the kinetics behavior of the reaction.

These models can quickly become obsolete and require maintenance when the operating conditions become significantly different from the design conditions.

Reliable predictive accident models (PAMs) (also referred to as safety performance functions (SPFs)) are essential to design and maintain safe road networks however, ongoing changes in road and vehicle design coupled with road safety initiatives, mean that these models can quickly become dated.

In the above age-structured predator prey population models, age structure is introduced into interactions of multi-species, and population models can quickly become remarkably complex [7, 13].

Our approach takes two stages: first to adopt an end-to-end stochastic training method that eventually forces the outputs of some channels to be constant, and then to prune those constant channels from the original neural network by adjusting the biases of their impacting layers such that the resulting compact model can be quickly fine-tuned.