Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...

Attention is a machine learning method that determines the importance of each component in a sequence relative to the other components in that sequence...

Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning...

The transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was...

In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can also improve the stability...

develops and studies methods and software that enable machines to perceive their environment and use learning and intelligence to take actions that maximize...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

Deep learning is a subset of machine learning that focuses on utilizing multilayered neural networks to perform tasks such as classification, regression...

open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms...

In machine learning, supervised learning (SL) is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired...

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

scale, and resource allocation when training a machine learning model. Comparison of deep learning software Differentiable programming All-Reduce Alex...

page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

The International Conference on Machine Learning (ICML) is a leading international academic conference in machine learning. Along with NeurIPS and ICLR,...

In statistics and machine learning, leakage (also known as data leakage or target leakage) is the use of information in the model training process which...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

AI (XAI), often overlapping with interpretable AI, or explainable machine learning (XML), is a field of research within artificial intelligence (AI) that...

In machine learning, a hyperparameter is a parameter that can be set in order to define any configurable part of a model's learning process. Hyperparameters...

are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables...

adapting to new information and responding to changing situations. Machine learning has been used for various scientific and commercial purposes including...

Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating...

non-human animals, and some machines; there is also evidence for some kind of learning in certain plants. Some learning is immediate, induced by a single...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...