Search results
Results from the WOW.Com Content Network
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
A large collection of Question to SPARQL specially design for Open Domain Neural Question Answering over DBpedia Knowledgebase. This dataset contains a large collection of Open Neural SPARQL Templates and instances for training Neural SPARQL Machines; it was pre-processed by semi-automatic annotation tools as well as by three SPARQL experts.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation learning.The field takes inspiration from biological neuroscience and is centered around stacking artificial neurons into layers and "training" them to process data.
An example is the prediction of linkages among entities in hyperedges of a hypergraph. In practice, to perform the aforementioned tasks, deep learning models designed for specific topological spaces must be constructed and implemented. These models, known as topological neural networks, are tailored to operate effectively within these spaces.
In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer.
The codebase for AlexNet was released under a BSD license, and had been commonly used in neural network research for several subsequent years. [20] [17] In one direction, subsequent works aimed to train increasingly deep CNNs that achieve increasingly higher performance on ImageNet.
Graph attention network is a combination of a graph neural network 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. A multi-head GAT layer can be expressed as follows: