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Generative representation learning tasks the model with producing the correct data to either match a restricted input or reconstruct the full input from a lower dimensional representation. [ 27 ] A common setup for self-supervised representation learning of a certain data type (e.g. text, image, audio, video) is to pretrain the model using ...
Machine learning (ML) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory. [1] In 1959, Arthur Samuel defined machine learning as a "field of study that gives computers the ability to learn without being explicitly programmed". [ 2 ]
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.
Group model s: some algorithms do not provide a refined model for their results and just provide the grouping information. Graph-based model s : a clique , that is, a subset of nodes in a graph such that every two nodes in the subset are connected by an edge can be considered as a prototypical form of cluster.
In pattern recognition and machine learning, a feature vector is an n-dimensional vector of numerical features that represent some object. Many algorithms in machine learning require a numerical representation of objects, since such representations facilitate processing and statistical analysis. When representing images, the feature values ...
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High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...
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