Whereas existing RNN models mainly focus on learning the dependency relationships in the input sequence while ignoring the ones in the output sequence.
The learning of these non-adjacent dependencies is much more difficult than learning adjacent dependencies.
Visualized using Gephi [50]. Figure 4 additionally shows an obtained mission dependency model in combination with the automatically learned resource dependency model.
We introduce in [11] an approach to automatically learn resource dependency model constantly from network traffic analyses, which we discuss later in Section 4.2.1 and present results in Section 4.2.
Specifically, we show the locality of learning-dependency and hierarchical modular structure in the 12 knowledge maps.
A knowledge map can be viewed as a directed graph, in which each node is a knowledge unit (KU), and each edge is a learning-dependency between two KUs.
Cotton-top tamarins (Newport et al. 2004) and rats (Murphy et al. 2008) can successfully learn abstract dependencies between elements at fixed distances, while chimpanzees can process dependencies between specific items at variable distances in the acoustic domain (Endress et al. 2010).
The paper finds that three elements of the innovation system literature are of particular interest for the practice of foresight: innovation systems and context dependency, learning and user producer interactions, and the role of knowledge and knowledge production.
Another approach is Graphical Gaussian Model (GGM) based on the concept of partial correlation for learning high-dimensional dependency networks from genomic data [ 16, 17], which is valid when number of genes is comparable to number of samples in the microarray data [ 18, 19].
Instead of designing a handcrafted a priori model on labels (such as an atlas in the medical domain), we propose to automatically learn the dependencies between labels.
In particular we use Naive Bayes classifiers and change the distribution from Gaussian to Cauchy, and use Gaussian Tree-Augmented Naive Bayes (TAN) classifiers to learn the dependencies among different facial motion features.
