Sentence examples for models trained to from inspiring English sources

ANN models trained to interpret reservoir, well, and completion information can predict well cumulative production with an acceptable degree of accuracy.

Y Combinator backed Plasticity is tackling the problem of getting software systems to better understand text, using deep learning models trained to understand what they're reading on Wikipedia articles — and offering an API for developers to enhance their own interfaces.

Table 1 Average performance of models trained to classify into Hominidae or one of the listed clades The table shows a comparison between IT and k-mers + sequence motif features.

Feedback control theory has also been used to adjust VM resource allocations, which are often based on models trained to identify the system and build the controller [26 29].

Researchers in Finland have their eye on this problem and have completed an interesting study that used EEG (electroencephalogram) sensors to monitor the brain signals of people reading the text of Wikipedia articles, combining that with machine learning models trained to interpret the EEG data and identify which concepts readers found interesting.

DBNs are graphical models trained to maximise the joint probability of a set of observed data and their conditional dependencies.

A model trained to catch color variations might still be vulnerable to attention-based adversarial images and vice versa.

We used the VLOG corpus to enhance a recognition model trained to recognize videos from the ELEA dataset.

ChemSpot employs a hybrid approach, in which the results of a CRF model trained to recognize IUPAC entities are combined with dictionary matching to find other chemical names.

Its special sauce is a deep learning model trained to be able to identify different voices and thus to separate out speakers within a transcript — meaning the user doesn't just get handed one big block of text.

These features are used as input to a Random Forest (RF) machine learning regression model trained to select the loop template with the lowest predicted distance from the target loop among a list of putative ones.