Sentence examples for characters embedded from inspiring English sources

Early this month, Mr. Villeneuve noticed an odd string of 22 characters embedded in files created by the malicious software and searched for it with Google.

But after that, pickings became piecemeal, with gay characters embedded in, but not always central to, shows like Glee, Modern Family and Brooklyn Nine-Nine.

In all MP analyses, multistate characters were treated as polymorphic, indel characters embedded in the sequence data matrix were treated as missing data (but were represented in an additional character matrix), and tree searches using PAUP [ 23] were heuristic using at least 200 random addition replicates and TBR branch-swapping.

Ms. Collins's novels are able to fuse all of these meanings into a credible character embedded in an exciting and complex story.

We build on the work of Zhang and LeCun [1] by providing a new character embedding methodology for text classification with CNNs, demonstrate that our new character embedding greatly reduces training time, memory use and improves classification performance.

We show that our character embedding greatly reduces computational costs and training time, and improves classification performance.

We demonstrate that our method of character embedding greatly reduces training time and memory use, while significantly improving classification performance.

For this purpose we propose a new character embedding offering greatly reduced memory consumption and training time.

This allows us to conduct tests for statistically significant differences in classification performance between the different character embedding methods.

We show that it outperforms the previous character embedding for the task of binary tweet sentiment classification, i.e. determining if tweets convey positive or negative sentiment.

The premise behind our representation is that instead of having a single non-zero value in our character embedding vector, we can have multiple non-zero values.