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The graph attention network (GAT) was introduced by Petar Veličković et al. in 2018. [11] Graph attention network is a combination of a GNN and an attention layer. The implementation of attention layer in graphical neural networks helps provide attention or focus to the important information from the data instead of focusing on the whole data.
The machine learning task for knowledge graph embedding that is more often used to evaluate the embedding accuracy of the models is the link prediction. [1] [3] [5] [6] [7] [18] Rossi et al. [5] produced an extensive benchmark of the models, but also other surveys produces similar results.
In order to allow the use of knowledge graphs in various machine learning tasks, several methods for deriving latent feature representations of entities and relations have been devised. These knowledge graph embeddings allow them to be connected to machine learning methods that require feature vectors like word embeddings. This can complement ...
Markov random fields find application in a variety of fields, ranging from computer graphics to computer vision, machine learning or computational biology, [13] [14] and information retrieval. [15] MRFs are used in image processing to generate textures as they can be used to generate flexible and stochastic image models.
Another examples is the Weisfeiler-Leman graph kernel [9] which computes multiple rounds of the Weisfeiler-Leman algorithm and then computes the similarity of two graphs as the inner product of the histogram vectors of both graphs. In those histogram vectors the kernel collects the number of times a color occurs in the graph in every iteration.
Automatically learning the graph structure of a Bayesian network (BN) is a challenge pursued within machine learning. The basic idea goes back to a recovery algorithm developed by Rebane and Pearl [7] and rests on the distinction between the three possible patterns allowed in a 3-node DAG:
An embedded graph uniquely defines cyclic orders of edges incident to the same vertex. The set of all these cyclic orders is called a rotation system.Embeddings with the same rotation system are considered to be equivalent and the corresponding equivalence class of embeddings is called combinatorial embedding (as opposed to the term topological embedding, which refers to the previous ...
In machine learning, the term "softmax" is credited to John S. Bridle in two 1989 conference papers, Bridle (1990a): [16]: 1 and Bridle (1990b): [3] We are concerned with feed-forward non-linear networks (multi-layer perceptrons, or MLPs) with multiple outputs.