Sentence examples for augmented the training from inspiring English sources

Additionally, to deal with the case where speech overlaps with acoustic events, we need such training data to have the neural network learn to discriminate in such a situation, and following the approach in [8], we have artificially augmented the training dataset to contain speech-overlapped data by adding publicly available speech from AURORA-4 [21].

Additionally, we also augmented the training set at the same time we used the entropy weighting; we call this hybrid the 'Combined Approach'.

We augmented the training set with 10, 50, 100, 200, 500 and 1000 mutated sequences for each original sequence in the training set, for both pointwise and pairwise mutation models.

We then augmented the training set with the point mutation model (Fig. 4), our new pairwise β-sheet model (Fig. 5) and using training sequences generated from both models simultaneously (Fig. 6). Figure 4 displays how the median AUC varies with the pointwise mutation rate.

American combat brigades, however, are to play a key role in augmenting the training and encouraging Iraqi units in the field to take more responsibility.

We employ layerwise training to Deep Convolutional Neural Networks (DCNN) in a supervised fashion and augment the training process with the RMSProp algorithm to achieve faster convergence.

Second, we suspect that augmenting the training data is also dealing with entropy effects at the same time as we are adding phylogenic information.

Because our method for augmenting the training data is randomized there is a legitimate concern that any reported result might vary each time the algorithm is run.

To augment the training and to help standardize the implementation of JIFF and meeting of goals, all teachers will receive an instruction list for using JIFF.

We postulated that the elimination of the beta-strand pairs SMURFLite must disregard because of computational complexity might be compensated for by augmenting the training data with artificial sequences based on likely mutations in those paired beta strands.

We have shown that incorporating some phylogenetic information by augmenting the training set using simulated evolution can improve these models in both sensitivity and specificity, and allow better predictions of distant homology.