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intelligence and cloud computing, compute is the amount of computing power or computational resources required to train machine learning models and large language
Compute_(machine_learning)
Subset of artificial intelligence
Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn
Machine_learning
Overview of and topical guide to machine learning
outline is provided as an overview of, and topical guide to, machine learning: Machine learning (ML) is a subfield of artificial intelligence within computer
Outline_of_machine_learning
Deep learning software
open-source machine learning library, a scientific computing framework, and a scripting language based on Lua. It provides LuaJIT interfaces to deep learning algorithms
Torch_(machine_learning)
Parameter-efficient fine-tuning technique for large language models
2026-01-22. "What is the cost of training large language models?". CUDO Compute. 2025-05-12. Retrieved 2026-01-22. "Optimization could cut the carbon footprint
LoRA_(machine_learning)
GPU computing platform used to accelerate machine learning and deep learning workloads Horovod — distributed training framework for deep learning Hugging
Comparison of machine learning software
Comparison_of_machine_learning_software
Interdisciplinary research area
Quantum machine learning (QML) is the study of quantum algorithms for machine learning. It often refers to quantum algorithms for machine learning tasks
Quantum_machine_learning
Academic conference in machine learning
The International Conference on Machine Learning (ICML) is an international academic conference in machine learning held annually since 1980. It is the
International Conference on Machine Learning
International_Conference_on_Machine_Learning
Algorithm for modelling sequential data
In deep learning, the transformer is a family of artificial neural network architectures based on the multi-head attention mechanism, in which text is
Transformer_(deep_learning)
Machine learning technique
In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence
Attention_(machine_learning)
Machine learning strategy
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)
Active learning (machine learning)
Active_learning_(machine_learning)
Topics referred to by the same term
computation. Compute may also refer to: Compute (machine learning), the amount of power required to train AI Compute! (1979–1983), often stylized as COMPUTE!, an
Compute_(disambiguation)
Research field that lies at the intersection of machine learning and computer security
Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. Machine learning techniques
Adversarial_machine_learning
page is a timeline of machine learning. Major discoveries, achievements, milestones, and other major events in machine learning are included. History
Timeline_of_machine_learning
Concept in machine learning
In machine learning, the term tensor informally refers to two different concepts: (i) a way of organizing data and (ii) a multilinear (tensor) transformation
Tensor_(machine_learning)
Set of methods for supervised statistical learning
In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms
Support_vector_machine
Process of automating the application of machine learning
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination
Automated_machine_learning
Programmable machine that processes data
efficiency of machine learning (and in particular of neural networks) has rapidly improved with progress in hardware for parallel computing, mainly graphics
Computer
Computation model defining an abstract machine
machines are useful models of real computers: Anything a real computer can compute, a Turing machine can also compute. For example: "A Turing machine
Turing_machine
Cloud-based Jupyter Notebook environment
free access to computing resources, including GPUs and TPUs, making it popular among researchers and students working on deep learning and data science
Google_Colab
Technique for the generative modeling of a continuous probability distribution
In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable
Diffusion_model
Computational model used in machine learning
In machine learning, a neural network (NN) or neural net, is a computational model inspired by the structure and functions of biological neural networks
Neural network (machine learning)
Neural_network_(machine_learning)
Ensemble learning method
In machine learning (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single
Boosting_(machine_learning)
Concept in machine learning
In statistics and machine learning, leakage (also known as data leakage or target leakage) refers to the use of information during model training that
Leakage_(machine_learning)
Machine learning technique
Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related
Transfer_learning
Academic journal
The Journal of Machine Learning Research is a peer-reviewed open access scientific journal covering machine learning. It was established in 2000 and the
Journal of Machine Learning Research
Journal_of_Machine_Learning_Research
Type of statistical inference
In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases
Transduction (machine learning)
Transduction_(machine_learning)
AI that learns decision rules from data
Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves
Rule-based_machine_learning
Amount of useful work accomplished by a computer
techniques and speedracer techniques. Algorithmic efficiency Compute (machine learning) Computer performance by orders of magnitude Network performance
Computer_performance
Computer scientist
He is a professor of computing science at the University of Alberta, fellow & Chief Scientific Advisor at the Alberta Machine Intelligence Institute
Richard_S._Sutton
Machine learning paradigm
In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based
Supervised_learning
Machine learning technique
In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization
Normalization (machine learning)
Normalization_(machine_learning)
Statistical law in machine learning
increased test-time compute (TTC), extending neural scaling laws beyond training to the deployment phase. In general, a deep learning model can be characterized
Neural_scaling_law
Execution of machine learning models on edge devices
Edge inference is the process of running machine learning or deep learning models on local devices (edge devices) such as smartphones, IoT devices, embedded
Edge_inference
Not-for-profit member-funded industry consortium
augmented reality, parallel computation, vision acceleration and machine learning. The open standards and associated conformance tests enable software
Khronos_Group
AI whose outputs can be understood by humans
(XAI), generally overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans
Explainable artificial intelligence
Explainable_artificial_intelligence
Machine-learning framework
transformation, designed for high-performance numerical computing and large-scale machine learning. It is developed by Google with contributions from Nvidia
JAX_(software)
Statistics and machine learning technique
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from
Ensemble_learning
Method of machine learning
In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update
Online_machine_learning
Measurement of algorithmic bias
Fairness in machine learning (ML) refers to the various attempts to correct algorithmic bias in automated decision processes based on ML models. Decisions
Fairness_(machine_learning)
Plot of machine learning model performance over time or experience
In machine learning (ML), a learning curve (or training curve) is a graphical representation that shows how a model's performance on a training set (and
Learning curve (machine learning)
Learning_curve_(machine_learning)
Research institute in high-performance computing
Svizzero di Calcolo Scientifico; CSCS) is the national high-performance computing centre of Switzerland. It was founded in Manno, canton Ticino, in 1991
Swiss National Supercomputing Centre
Swiss_National_Supercomputing_Centre
Optimization algorithm for artificial neural networks
In machine learning, backpropagation is a gradient computation method commonly used for training a neural network in computing parameter updates. It is
Backpropagation
Field of machine learning
Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. While supervised learning and
Reinforcement_learning
Decentralized machine learning
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients)
Federated_learning
In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers
Kernel_perceptron
Branch of machine learning
In machine learning, deep learning (DL) focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation
Deep_learning
Academic conference in machine learning
The International Conference on Learning Representations (ICLR) is a machine learning conference typically held in late April or early May each year.
International Conference on Learning Representations
International_Conference_on_Learning_Representations
American artificial intelligence researcher
Engineers in 2019 for "contributions to machine learning algorithms and systems" and a Fellow of the Association for Computing Machinery in 2022 for "contributions
Eric_Xing
Deep learning artificial intelligence research team
Formed in 2011, it combined open-ended machine learning research with information systems and large-scale computing resources. It created tools such as TensorFlow
Google_Brain
Researcher and Professor of computing
the Bren Professor of Computing at California Institute of Technology. Previously, she was a senior director of Machine Learning research at NVIDIA and
Anima_Anandkumar
Use of machine learning to rank items
Learning to rank (LTR) or machine-learned ranking (MLR) is the application of machine learning, often supervised, semi-supervised or reinforcement learning
Learning_to_rank
Aspect of computational complexity theory
solve a particular problem. Compute (machine learning) Gregory J., Chaitin (1966). "On the Length of Programs for Computing Finite Binary Sequences" (PDF)
Computational_resource
Class of algorithms for pattern analysis
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These
Kernel_method
Canadian physicist and entrepreneur
mathematical physicist, quantum computing researcher, entrepreneur, and writer who is a key contributor of Google's quantum machine learning software, Tensorflow
Guillaume_Verdon
Cloud-based service and infrastructure
computing services offered by Google that provides a series of modular cloud services including computing, data storage, data analytics, and machine learning
Google_Cloud_Platform
Machine learning algorithm
Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or
Decision_tree_learning
Theory of machine learning
Theoretical results in machine learning often focus on a type of inductive learning known as supervised learning. In supervised learning, an algorithm is provided
Computational_learning_theory
machine learning (ML) research and have been cited in peer-reviewed academic journals. Datasets are an integral part of the field of machine learning
List of datasets for machine-learning research
List_of_datasets_for_machine-learning_research
Tuning parameter (hyperparameter) in optimization
In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration
Learning_rate
Measure of computer performance
computes at 847 teraFLOPS. As of June 2020[update], GIMPS, searching for Mersenne primes, is sustaining 1,354 teraFLOPS. Compute (machine learning) Computer
Floating point operations per second
Floating_point_operations_per_second
Class of artificial neural network
Boltzmann machines, in particular the gradient-based contrastive divergence algorithm. Restricted Boltzmann machines can also be used in deep learning networks
Restricted_Boltzmann_machine
Type of artificial neural network
learning machines are feedforward neural networks for classification, regression, clustering, sparse approximation, compression and feature learning with
Extreme_learning_machine
Deep learning training framework
2025. "Distributed Deep Learning with Horovod". Purdue University Research Computing. Retrieved November 28, 2025. "Deep learning with Apache Spark and
Horovod_(machine_learning)
Machine learning paradigm
Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals
Self-supervised_learning
Machine learning methods using multiple input modalities
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
Multimodal_learning
Software for understanding biological data
Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems
Machine learning in bioinformatics
Machine_learning_in_bioinformatics
– scientific computing framework with support for machine learning and deep learning algorithms Apache Mahout — scalable machine learning library for big
Lists of open-source artificial intelligence software
Lists_of_open-source_artificial_intelligence_software
Model-free reinforcement learning algorithm
that the algorithm computes: the expected reward—that is, the quality—of an action taken in a given state. Reinforcement learning involves an agent, a
Q-learning
Hardware acceleration unit for artificial intelligence tasks
deep learning processor, is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence and machine learning
Neural_processing_unit
Tabular comparison of deep learning software
under different licenses [further explanation needed] Comparison of machine learning software Comparison of statistical packages Comparison of cognitive
Comparison of deep learning software
Comparison_of_deep_learning_software
University academic program
methods, cloud computing, machine learning, programming theory and paradigms. Modern academic programs also cover emerging computing fields like Artificial
Computer science and engineering
Computer_science_and_engineering
In machine learning, instance-based learning (sometimes called memory-based learning) is a family of learning algorithms that, instead of performing explicit
Instance-based_learning
Technique in machine learning
Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"
Curriculum_learning
Machine learning technique
Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous
Mixture_of_experts
Open source platform
computations across a distributed computing environment. It offers implementations for numerous statistical and machine learning algorithms, which are accessible
H2O_(software)
Integrated circuit technology
prioritize robustness, adaptability, and learning by emulating the brain’s distributed processing across small computing elements. This interdisciplinary field
Neuromorphic_computing
Computer hardware technology that uses quantum mechanics
Wikimedia Commons Learning materials related to Quantum computing at Wikiversity Stanford Encyclopedia of Philosophy: "Quantum Computing" by Amit Hagar and
Quantum_computing
Measurable property or characteristic
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating
Feature_(machine_learning)
Intelligence of machines
brain cells and brain organoids for intelligent computing Outline of deep learning Outline of machine learning Pseudorandomness – Appearing random but actually
Artificial_intelligence
Applications of machine learning to quantum physics
fields. Quantum computing Quantum machine learning Quantum annealing Quantum neural network HHL Algorithm Comparison of machine learning software Torlai
Machine_learning_in_physics
Set of learning techniques in machine learning
In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations
Feature_learning
Quantum computing company based in Toronto, Canada
framework that melds machine learning with quantum computing". SiliconANGLE. 2021-02-17. Retrieved 2021-03-02. "CDL quantum machine learning program partners
Xanadu_Quantum_Technologies
Decision-making process conducted with varying degrees of human oversight
capacity and computational power with GPU coprocessors and cloud computing. Machine learning systems based on foundation models run on deep neural networks
Automated_decision-making
Object categorization problem
One-shot learning is a problem setup in machine learning which leverages a singular example to assist with classification, originating in the field of
One-shot learning (computer vision)
One-shot_learning_(computer_vision)
American government agency
agencies. Notable IARPA investments include quantum computing, superconducting computing, machine learning, and forecasting tournaments. IARPA characterizes
Intelligence Advanced Research Projects Activity
Intelligence_Advanced_Research_Projects_Activity
Semi-annual technology conference held by Nvidia
and deep learning, including: self-driving cars, healthcare, high performance computing, professional visualization, and Nvidia Deep Learning Institute
Nvidia_GTC
Machine learning technique
In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Subfield of machine learning, intelligent control, and control theory
Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems
Machine_learning_control
Emotion modeling in AI
haptic feedback can shape human reward learning and mobile interaction behavior, suggesting that affective computing systems may not only interpret emotional
Affective_computing
American semiconductor company
Taps AI Machine With Massive Chip to Fight Coronavirus". The Wall Street Journal. ISSN 0099-9660. "Cerebras Systems and NETL Set New Compute Milestone"
Cerebras_Systems
Machine learning that combines deep learning and reinforcement learning
Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem
Deep_reinforcement_learning
of machine learning (ML) in earth sciences include geological mapping, gas leakage detection and geological feature identification. Machine learning is
Machine learning in earth sciences
Machine_learning_in_earth_sciences
Technique to make a model more generalizable and transferable
mathematics, statistics, finance, and computer science, particularly in machine learning and inverse problems, regularization is a process that converts the
Regularization_(mathematics)
Programming paradigm
scientific computing and machine learning. One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms
Differentiable_programming
Research field in deep learning
deep learning (TDL) is a research field that extends deep learning to handle complex, non-Euclidean data structures. Traditional deep learning models
Topological_deep_learning
Specialist field of computer science
science Geographic information system (GIS) High-performance computing Machine learning Network analysis Neuroinformatics Numerical linear algebra Numerical
Computational_science
Cloud gaming service
high-performance computing (HPC). Its infrastructure work includes participation in European data center projects for artificial intelligence, machine learning, high-performance
Boosteroid
Statistical technique for producing prediction sets
predictor (whether statistical, machine learning, or deep learning) only assuming exchangeability of the data. CP works by computing "nonconformity scores" on
Conformal_prediction
Model-free reinforcement learning algorithm
_{k}\right)} in the environment. Compute rewards-to-go[clarification needed] R ^ t {\textstyle {\hat {R}}_{t}} . Compute advantage[clarification needed]
Proximal_policy_optimization
COMPUTE MACHINE-LEARNING
COMPUTE MACHINE-LEARNING
Female
Yiddish
(×™Ö·×—Ö°× Ö¶×¢) Yiddish form of Hebrew Yochana, YACHNE means "God is gracious."Â
Female
French
French feminine form of Latin Martinus, MARTINE means "of/like Mars."Â
Male
Hindi/Indian
(सचिन) Hindi myth name borne by Indra, SACHIN means "pure."
Female
English
Feminine form of English Max, MAXINE means either "the greatest rival" or "the stream of Mack."Â
Male
English
Variant spelling of English unisex Macey, MACIE means "gift of God."
Surname or Lastname
English
English : variant spelling of Machen.Spanish (MachÃn) : probably a nickname from machÃn ‘boor’, ‘lout’, often applied to a blacksmith’s apprentice.French : nickname from Old French machin ‘scheming’.
Female
English
Variant spelling of English Maureen, MAURINE means "obstinacy, rebelliousness" or "their rebellion."
Girl/Female
Bengali, Indian
Machine
Boy/Male
American, Australian
Weighing Machine
Male
Scottish
Pet form of Scottish Gaelic Lachlann, LACHIE means "lake-land."
Female
German
German form of Scottish Malvina, MALWINE means "smooth-brow."
Female
Native American
Native American Hopi name KACHINA means "sacred dancer; spirit."
Male
French
French form of Latin Macarius, MACAIRE means "blessed."
Female
French
Feminine form of French Marin, MARINE means "of the sea."
Male
Hebrew
Variant spelling of Hebrew Yakiyn, YACHIN means "he establishes" or "whom God strengthens."Â
Female
Scottish
Feminine form of Scottish Lachlan, LACHINA means "lake-land."
Male
English
Pet form of English Sacheverell, SACHIE means "roe-buck leap."
Surname or Lastname
English
English : occupational name for a stonemason, Anglo-Norman French machun, a Norman dialect variant of Old French masson (see Mason).
Girl/Female
Australian, Japanese
Child of Machi
Female
Hawaiian
Hawaiian name MAHINA means "moon; moonlight."
COMPUTE MACHINE-LEARNING
COMPUTE MACHINE-LEARNING
Girl/Female
Latin
Blooming.
Boy/Male
Australian, Danish, German, Greek, Scandinavian, Swedish, Swiss
Stone; Rock
Girl/Female
Indian, Marathi
Flower; Awesome
Boy/Male
African, German, Gujarati, Hindu, Indian, Malayalam, Tamil, Telugu, Turkish
My Father; All Given to Good
Surname or Lastname
English (Norfolk)
English (Norfolk) : habitational name from a place in Norfolk, named in Old English with þeodo- ‘people’ + ford ‘ford’.
Girl/Female
Indian
Sky; Beautiful Girl
Boy/Male
Hindu
Girl/Female
Hebrew
Father of many.
Girl/Female
Hindu, Indian, Traditional
Auspicious; Bliss
Boy/Male
Bengali, Hindu, Indian, Kannada, Mythological, Sanskrit, Telugu
A Character of Mahabharata; Son of King Shantanu
COMPUTE MACHINE-LEARNING
COMPUTE MACHINE-LEARNING
COMPUTE MACHINE-LEARNING
COMPUTE MACHINE-LEARNING
COMPUTE MACHINE-LEARNING
n.
A combination of persons acting together for a common purpose, with the agencies which they use; as, the social machine.
n.
One who computes.
n.
One who commutes; especially, one who commutes in traveling.
n.
One who or operates a machine; a machinist.
imp. & p. p.
of Compute
pl.
of Tachina
v. t.
To exchange; to put or substitute something else in place of, as a smaller penalty, obligation, or payment, for a greater, or a single thing for an aggregate; hence, to lessen; to diminish; as, to commute a sentence of death to one of imprisonment for life; to commute tithes; to commute charges for fares.
v. t.
To wind marline around; as, to marline a rope.
v. t.
To write; to compose.
v. t.
To compose; to settle; to arrange.
n.
Machines, in general, or collectively.
v. t.
To compute; to count.
imp. & p. p.
of Commute
imp. & p. p.
of Machine
a.
Of or pertaining to machines.
n.
The working parts of a machine, engine, or instrument; as, the machinery of a watch.
v. t.
To subject to the action of machinery; to effect by aid of machinery; to print with a printing machine.
imp. & p. p.
of Compete