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Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. [1][2][3] Statistical learning theory deals with the statistical inference problem of finding a predictive function based on data. Statistical learning theory has led to successful applications in fields such as ...
A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI. [1][2] The concept was initially developed by Ian Goodfellow and his colleagues in June 2014. [3] In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain ...
e. Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data and thus perform tasks without explicit instructions. [1] Recently, artificial neural networks have been able to surpass many previous approaches in ...
Google JAX is a machine learning framework for transforming numerical functions. [69] [70] [71] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).
PyTorch is a machine learning library based on the Torch library, [4] [5] [6] used for applications such as computer vision and natural language processing, [7] originally developed by Meta AI and now part of the Linux Foundation umbrella.
e. In computational learning theory, probably approximately correct (PAC) learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant. [1] In this framework, the learner receives samples and must select a generalization function (called the hypothesis) from a certain class of possible functions.
Federated learning (also known as collaborative learning) is a sub-field of machine learning focusing on settings in which multiple entities (often referred to as clients) collaboratively train a model while ensuring that their data remains decentralized. [1] This stands in contrast to machine learning settings in which data is centrally stored ...
Machine learning involves the study and construction of algorithms that can learn from and make predictions on data. [3] These algorithms operate by building a model from an example training set of input observations to make data-driven predictions or decisions expressed as outputs, rather than following strictly static program instructions.