The contribution of our model is that hierarchical feature evolution and nonlinear feature mapping are jointly optimized in a unified and perceptual-aware deep network (DNCSAE), which well resembles several important visual properties, i.e., hierarchy, sparsity, and non-negativity.
As shown in Fig. 10, the following steps are performed for each hypothesis: the hypothesis and the feature maps are jointly used to generate population-coded representations of hypothesis features (see Fig. 3), in this case distance, elevation, image position and distance-to-free-area.
The influence of grouping cues on the spread of attention section above reviewed the results of Kahneman and Henik (1981), who demonstrated that Gestalt grouping cues determine the features that are jointly reported in partial report tasks, presumably because these cues determine the spread of attention (for a similar view, see Prinzmetal, 1981).
Here we present a new method, D iscern, which provides a significant improvement over the state-of-the-art through the use of statistical techniques to derive a model with a small set of features that are jointly predictive of enzyme active sites.
The network is globally tuned through back-propagation and all the parameters of both feature extraction and classification are jointly optimized.
In the current DNN, the tissue identities are inputs and are jointly represented by hidden variables together with genomic features.
The gates are jointly controlled by a bottom-up system favoring locations with unique features and a top-down system favoring locations with features designated as target features.
Multi-task learning is a statistical spatial feature mapping framework where identical input data from different tasks are jointly learned.
Sometimes, they are jointly under threat.
Yes, sRNAs are jointly transcribed.
Union formation and fertility are jointly determined.
