Sentence examples for model learning as from inspiring English sources

The reason is that the sparsifying dictionary actually plays a role in the regularization of the model learning, as the dictionary basis vectors are numerical representations of patient heterogeneity.

This makes sense, since enforcing the constraint that the individual CHI model should be represented by a dictionary plays a role in the regularization of the model learning, as the dictionary basis vectors are numerical representations of patient heterogeneity.

Therefore, model learning as well as learning social-emotional competence in the early years is essential to prevent problematic behavior in multi-ethnic classrooms.

We acquired six 3D CAD models for each of the six target classes (APCs, tanks, pick-ups, cars, minivans, SUVs) for model learning, as shown in Figure 5.

Based on the basic framework of graph-based SSL, SSL Co-training obtains more labeled data by assigning labels to unlabeled data, i.e., "pseudo-labels," and uses them for model learning as if they were labeled.

Classical computationalists can also cite the enormous success of Bayesian decision theory, which models learning as probabilistic updating.

So the model learns that as well, and this can be deployed separately as a computer vision model for finding out where a pet (or small legged robot) can get to in a given image.

NIGHTWATCH has not pushed model learning to AWS as the company prefers a complete understanding of the memory, CPU, and GPU (NVIDIA CUDA libraries near future) requirements before selecting the appropriate cloud infrastructure compute needs and cost).

According to this model, learning disabilities arise as a result of the interaction between multiple cognitive risk and protective factors that are influenced by both genes and the environment during development.

Compared to QME, which uses a genetic algorithm as its model learning strategy, the proposed algorithm can better deal with search spaces with multimodal fitness landscapes.

"A fundamental shift is needed towards a more personalised, social, open, dynamic, emergent and knowledge-pull model for learning, as opposed to the one-size-fits-all, centralised, static, top-down and knowledge-push models of traditional learning solutions".